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      <image:caption>Yapay zekânın tanımına, 70 yıllık tarihine ve günümüzdeki rolüne kapsamlı bir giriş. Klasik tanımları, AI Winter dönemlerini, derin öğrenme devrimini ve LLM çağını tek dersten anlayın.</image:caption>
      <image:title>Yapay Zeka Nedir? Tanım, Tarihçe ve Bugünün Manzarası</image:title>
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      <image:caption>Yapay zekânın tanımına, 70 yıllık tarihine ve günümüzdeki rolüne kapsamlı bir giriş. Klasik tanımları, AI Winter dönemlerini, derin öğrenme devrimini ve LLM çağını tek dersten anlayın.</image:caption>
      <image:title>Yapay Zeka Nedir? Tanım, Tarihçe ve Bugünün Manzarası</image:title>
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      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&amp;q=80</image:loc>
      <image:caption>Üç kavram en sık karıştırılanlardan: Yapay Zeka (AI), Makine Öğrenmesi (ML) ve Derin Öğrenme (DL). Bu derste hiyerarşiyi netleştirip pratik kararlar vermenize yardım edecek bir karar ağacı çıkaracağız.</image:caption>
      <image:title>AI vs ML vs DL: Doğru Hiyerarşi ve Pratik Sonuçları</image:title>
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      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&amp;q=80</image:loc>
      <image:caption>Üç kavram en sık karıştırılanlardan: Yapay Zeka (AI), Makine Öğrenmesi (ML) ve Derin Öğrenme (DL). Bu derste hiyerarşiyi netleştirip pratik kararlar vermenize yardım edecek bir karar ağacı çıkaracağız.</image:caption>
      <image:title>AI vs ML vs DL: Doğru Hiyerarşi ve Pratik Sonuçları</image:title>
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      <image:caption>Makine öğrenmesinin üç ana yaklaşımı: etiketli veriden öğrenen supervised, etiketsiz veride yapı arayan unsupervised ve ödül sinyaliyle öğrenen reinforcement. Her birini gerçek kod örnekleri ve canlı çalıştırılabilir Pyodide blokları ile inceliyoruz.</image:caption>
      <image:title>Makine Öğrenmesinin 3 Paradigması: Supervised, Unsupervised, Reinforcement</image:title>
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      <image:caption>Makine öğrenmesinin üç ana yaklaşımı: etiketli veriden öğrenen supervised, etiketsiz veride yapı arayan unsupervised ve ödül sinyaliyle öğrenen reinforcement. Her birini gerçek kod örnekleri ve canlı çalıştırılabilir Pyodide blokları ile inceliyoruz.</image:caption>
      <image:title>Makine Öğrenmesinin 3 Paradigması: Supervised, Unsupervised, Reinforcement</image:title>
    </image:image>
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      <image:caption>Şimdi gerçek bir model eğitelim. Iris veri seti üzerinde 3-sınıf classifier yapacak, overfitting&apos;in nasıl tespit edildiğini görecek ve cross-validation kullanacaksınız. Tüm kod tarayıcıda çalışır.</image:caption>
      <image:title>İlk Yapay Zeka Modeliniz: Iris Çiçeği Sınıflandırıcı</image:title>
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      <image:caption>Şimdi gerçek bir model eğitelim. Iris veri seti üzerinde 3-sınıf classifier yapacak, overfitting&apos;in nasıl tespit edildiğini görecek ve cross-validation kullanacaksınız. Tüm kod tarayıcıda çalışır.</image:caption>
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      <image:caption>AI etiğinin neden önemli olduğunu, ünlü vakaları (COMPAS, Amazon recruiting), düzenleyici çerçeveleri (EU AI Act, NIST RMF, OECD ilkeleri) ve sorumlu AI inşa etmek için pratik kontrol listelerini öğrenin.</image:caption>
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      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&amp;q=80</image:loc>
      <image:caption>AI etiğinin neden önemli olduğunu, ünlü vakaları (COMPAS, Amazon recruiting), düzenleyici çerçeveleri (EU AI Act, NIST RMF, OECD ilkeleri) ve sorumlu AI inşa etmek için pratik kontrol listelerini öğrenin.</image:caption>
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      <image:caption>ChatGPT&apos;den (Kasım 2022) bugüne yapay zekânın yüzü değişti. Bu derste modern üretken AI&apos;nin temel taşı olan transformer mimarisini, LLM&apos;lerin nasıl eğitildiğini, prompt engineering ile RAG&apos;in pratiğini, fine-tuning ne zaman doğru seçim olduğunu ve 2025-2026&apos;nın ana akımı haline gelen agentic sistemleri uçtan uca öğreneceksiniz.</image:caption>
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      <image:title>macOS&apos;a Python Kurulumu: System Python&apos;a Neden Dokunmuyoruz, Homebrew ve Modern Yollar</image:title>
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      <image:caption>macOS&apos;ta zaten &apos;python3&apos; var ama Apple onu sistem işleri için tutuyor — sen ona dokunma. Bu derste Homebrew, asdf, pyenv ve resmi installer arasındaki tercihi yapmayı, sürüm yönetiminin gizli inceliklerini öğreneceksin.</image:caption>
      <image:title>macOS&apos;a Python Kurulumu: System Python&apos;a Neden Dokunmuyoruz, Homebrew ve Modern Yollar</image:title>
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      <image:title>Linux&apos;a Python Kurulumu: Ubuntu, Fedora, Arch, Alpine ve Kaynaktan Derleme</image:title>
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      <image:caption>Linux&apos;ta Python kurmak hem en kolay hem en kafa karıştırıcı yol. Sistem zaten bir python3 ile geliyor ama bu seninki değil. Bu derste Ubuntu/Debian, Fedora, Arch ve Alpine için adım adım, sonra kaynaktan derlemeyi (./configure --enable-optimizations) öğreniyoruz. VPS&apos;e deploy senaryosuna kadar uçuyoruz.</image:caption>
      <image:title>Linux&apos;a Python Kurulumu: Ubuntu, Fedora, Arch, Alpine ve Kaynaktan Derleme</image:title>
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      <image:title>pyenv ile Çoklu Python Sürümü Yönetimi: Sürümler Arası &apos;Anahtar&apos; Olma Sanatı</image:title>
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      <image:caption>Bir projende 3.10, başka projende 3.13 lazım. CI&apos;da 3.11 test ediyorsun. Production&apos;da 3.12. Bunların hepsini tek Linux/macOS makinende sürdürmek pyenv ile saniyeler meselesi. Bu ders pyenv&apos;in tüm komutlarını, .python-version dosyasını, virtualenv plugin&apos;ini ve gerçek senaryolarını işliyor.</image:caption>
      <image:title>pyenv ile Çoklu Python Sürümü Yönetimi: Sürümler Arası &apos;Anahtar&apos; Olma Sanatı</image:title>
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      <image:title>Python REPL&apos;i Etkili Kullanma: Keşif, Prototip ve Hata Ayıklamanın Sessiz Sanatı</image:title>
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      <image:caption>IPython sadece daha güzel renkli bir REPL değil. %timeit ile mikrosaniye benchmark, ?? ile kaynak kodu görme, !ls ile shell escape, %save ile dosyaya yazma — bütün veri bilimi/AI dünyası bu komutlar üzerinde dönüyor. Bu ders sana &apos;günlük 50 kez kullanacağın&apos; magic&apos;leri tek tek öğretir.</image:caption>
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      <image:caption>AI ve veri bilimi öğrenmek istiyorsan Jupyter&apos;i bilmiyor olmak imkansız. Bu ders sıfırdan başlatma, hücre tipleri, magic commands, görselleştirme entegrasyonu, paylaşma yolları (Colab/Kaggle/Binder), git ile sürüm kontrolü problemleri ve production&apos;a Jupyter taşımanın neden kötü fikir olduğunu konuşuyor.</image:caption>
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      <image:caption>İlk gerçek .py dosyanı yazıyorsun. Modül 1&apos;in capstone&apos;u: shebang, encoding declaration, if __name__ == &apos;__main__&apos;, python -m flag&apos;i, sys.argv, exit code&apos;lar, .pyc cache, ve her gün karşılaşacağın komut satırı pratikleri. 4 hands-on script ile bitiyoruz.</image:caption>
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      <image:caption>İlk gerçek .py dosyanı yazıyorsun. Modül 1&apos;in capstone&apos;u: shebang, encoding declaration, if __name__ == &apos;__main__&apos;, python -m flag&apos;i, sys.argv, exit code&apos;lar, .pyc cache, ve her gün karşılaşacağın komut satırı pratikleri. 4 hands-on script ile bitiyoruz.</image:caption>
      <image:title>İlk Python Script&apos;in: hello.py&apos;den python -m&apos;e Komut Satırının İncelikleri</image:title>
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      <image:caption>Diğer dillerde değişken bir &apos;kutu&apos;dur — değer kutuya konur. Python&apos;da değişken bir &apos;etiket&apos;tir — nesneye yapıştırılır. Bu fark gibi görünmeyebilir ama Python&apos;un %30&apos;unun davranışını açıklıyor: mutable default tuzağı, list aliasing, fonksiyon parametre semantics, garbage collection. Bu derste &apos;a = 5 yazınca aslında ne olur&apos; sorusunun derinlemesine cevabını alıyoruz.</image:caption>
      <image:title>Değişkenler: Python&apos;da &apos;Etiket vs Kutu&apos; Felsefesi ve Assignment&apos;ın İçsel Gerçeği</image:title>
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      <image:caption>Diğer dillerde değişken bir &apos;kutu&apos;dur — değer kutuya konur. Python&apos;da değişken bir &apos;etiket&apos;tir — nesneye yapıştırılır. Bu fark gibi görünmeyebilir ama Python&apos;un %30&apos;unun davranışını açıklıyor: mutable default tuzağı, list aliasing, fonksiyon parametre semantics, garbage collection. Bu derste &apos;a = 5 yazınca aslında ne olur&apos; sorusunun derinlemesine cevabını alıyoruz.</image:caption>
      <image:title>Değişkenler: Python&apos;da &apos;Etiket vs Kutu&apos; Felsefesi ve Assignment&apos;ın İçsel Gerçeği</image:title>
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      <image:caption>Bir Python kodu açtığında 30 saniye içinde &apos;profesyonel mi yoksa amatör mü&apos; anlayabilirsin — isimlendirme. snake_case, PascalCase, UPPER_SNAKE, _private, __mangling. Bu derste her tipin nerede kullanıldığını, neden bu konvansiyon olduğunu, ruff ile otomatize etmeyi ve Türkçe değişken adı tartışmasını işliyoruz.</image:caption>
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      <image:caption>Bir Python kodu açtığında 30 saniye içinde &apos;profesyonel mi yoksa amatör mü&apos; anlayabilirsin — isimlendirme. snake_case, PascalCase, UPPER_SNAKE, _private, __mangling. Bu derste her tipin nerede kullanıldığını, neden bu konvansiyon olduğunu, ruff ile otomatize etmeyi ve Türkçe değişken adı tartışmasını işliyoruz.</image:caption>
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      <image:caption>0.1 + 0.2 == 0.3 yazınca Python False döner. Bu bug değil — IEEE 754 standardının 1985&apos;ten beri yaşayan gerçeği. Bu derste float&apos;un içsel yapısını (sign+exponent+mantissa), neden bazı sayıların binary&apos;de &apos;sonsuz tekrar ettiğini&apos;, math.isclose&apos;un nasıl kullanıldığını, NaN/inf davranışlarını, AI/ML&apos;de neden float32 baskın olduğunu ve finansal hesapta float&apos;tan kaçınmanın hayati önemini öğreniyoruz.</image:caption>
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      <image:caption>0.1 + 0.2 == 0.3 yazınca Python False döner. Bu bug değil — IEEE 754 standardının 1985&apos;ten beri yaşayan gerçeği. Bu derste float&apos;un içsel yapısını (sign+exponent+mantissa), neden bazı sayıların binary&apos;de &apos;sonsuz tekrar ettiğini&apos;, math.isclose&apos;un nasıl kullanıldığını, NaN/inf davranışlarını, AI/ML&apos;de neden float32 baskın olduğunu ve finansal hesapta float&apos;tan kaçınmanın hayati önemini öğreniyoruz.</image:caption>
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      <image:caption>Java/C# karmaşık sayı için kütüphane gerektirir; Python `j` syntax&apos;iyle dilin parçası yapmış. Bu ders: complex&apos;in matematiksel altyapısı, polar/kartezyen dönüşüm, cmath modülünün gücü, 2D rotation matrisi yerine complex çarpma kısayolu, sinyal işleme + Mandelbrot fraktalı + AC devre analizi gibi gerçek uygulamalar.</image:caption>
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      <image:caption>Float&apos;la para hesaplamak — yıllar içinde bankaları sallayan klasik bug kaynağı. Python&apos;un `decimal` modülü bu sorunu çözer: tam ondalık precision, kontrollü yuvarlama (ROUND_HALF_UP, ROUND_HALF_EVEN), context yönetimi. Bu derste: TR KDV hesabı, döviz çevirici, e-ticaret sepeti, PostgreSQL NUMERIC entegrasyonu — gerçek production pattern&apos;leri.</image:caption>
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      <image:caption>Float&apos;la para hesaplamak — yıllar içinde bankaları sallayan klasik bug kaynağı. Python&apos;un `decimal` modülü bu sorunu çözer: tam ondalık precision, kontrollü yuvarlama (ROUND_HALF_UP, ROUND_HALF_EVEN), context yönetimi. Bu derste: TR KDV hesabı, döviz çevirici, e-ticaret sepeti, PostgreSQL NUMERIC entegrasyonu — gerçek production pattern&apos;leri.</image:caption>
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      <image:caption>Float 0.1&apos;i tam saklayamaz. Decimal saklar ama 1/3&apos;ü değil. Fraction modülü bütün rasyonel sayıları **kesirli** saklayarak tam matematik yapıyor. Bu derste: müzik teorisinde armoni oranları, mutfakta tarif ölçeklendirme, geometrik hesap, bilim simülasyonlarında precision kurtarma — niş ama bilinmesi değerli bir araç.</image:caption>
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    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>Float 0.1&apos;i tam saklayamaz. Decimal saklar ama 1/3&apos;ü değil. Fraction modülü bütün rasyonel sayıları **kesirli** saklayarak tam matematik yapıyor. Bu derste: müzik teorisinde armoni oranları, mutfakta tarif ölçeklendirme, geometrik hesap, bilim simülasyonlarında precision kurtarma — niş ama bilinmesi değerli bir araç.</image:caption>
      <image:title>fractions Modülü: Tam Hassas Rasyonel Sayılar — 1/3 + 1/6 = 0.5 Kanıtla</image:title>
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    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>Python&apos;da `True` aslında `int`&apos;in alt sınıfı (`True + 1 == 2`!). Her tip &apos;doğru/yanlış&apos; bağlamında değerlendirilebilir — buna &apos;truthiness&apos; denir. None ise &apos;değer yok&apos; anlamına gelen tek-elemanlı bir sentinel. Bu derste: bool gerçek doğası, falsy değerler tablosu, `is None` vs `== None`, Optional type hint, default arg sentinel pattern, ve günlük kodda en sık karşına çıkan &apos;küçük&apos; detayların derinliği.</image:caption>
      <image:title>bool ve None: Truthiness&apos;in Felsefesi ve Sentinel Değer Sanatı</image:title>
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    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/python-programlama/python-bool-none-truthiness"/>
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    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>Python&apos;da `True` aslında `int`&apos;in alt sınıfı (`True + 1 == 2`!). Her tip &apos;doğru/yanlış&apos; bağlamında değerlendirilebilir — buna &apos;truthiness&apos; denir. None ise &apos;değer yok&apos; anlamına gelen tek-elemanlı bir sentinel. Bu derste: bool gerçek doğası, falsy değerler tablosu, `is None` vs `== None`, Optional type hint, default arg sentinel pattern, ve günlük kodda en sık karşına çıkan &apos;küçük&apos; detayların derinliği.</image:caption>
      <image:title>bool ve None: Truthiness&apos;in Felsefesi ve Sentinel Değer Sanatı</image:title>
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    <loc>https://sukruyusufkaya.com/learn/python-programlama/python-aritmetik-operatorler-operator-overloading</loc>
    <lastmod>2026-05-10T14:32:09.817Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
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    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>+ ve - tek satırda Vector toplayabiliyor mu? Money * 1.18 ile KDV hesaplayabiliyor mu? Python&apos;un magic method&apos;ları (__add__, __sub__, __mul__, __radd__) sayesinde evet. Bu derste: 7 aritmetik operatör derinlemesine, augmented assignment, NotImplemented sentinel&apos;ı, sıralı tip dönüşümü, ve gerçek Vector + Money sınıfları.</image:caption>
      <image:title>Aritmetik Operatörler ve Operator Overloading: Vector(1,2) + Vector(3,4) Mucizesi</image:title>
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    <lastmod>2026-05-10T14:32:09.817Z</lastmod>
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    <priority>0.60</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/python-programlama/python-aritmetik-operatorler-operator-overloading"/>
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    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>+ ve - tek satırda Vector toplayabiliyor mu? Money * 1.18 ile KDV hesaplayabiliyor mu? Python&apos;un magic method&apos;ları (__add__, __sub__, __mul__, __radd__) sayesinde evet. Bu derste: 7 aritmetik operatör derinlemesine, augmented assignment, NotImplemented sentinel&apos;ı, sıralı tip dönüşümü, ve gerçek Vector + Money sınıfları.</image:caption>
      <image:title>Aritmetik Operatörler ve Operator Overloading: Vector(1,2) + Vector(3,4) Mucizesi</image:title>
    </image:image>
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    <loc>https://sukruyusufkaya.com/learn/python-programlama/python-karsilastirma-operatorleri-total-ordering</loc>
    <lastmod>2026-05-10T14:44:11.235Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
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    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>&lt; &gt; == != &lt;= &gt;= görünüşte basit ama Python&apos;da chained comparison (`0 &lt; x &lt; 10`), her tip için custom karşılaştırma, ve `@total_ordering` decorator gibi süslü özellikler var. Bu derste: 6 karşılaştırma operatörü derinlemesine, custom sortable class yapımı, __hash__ ve __eq__ kontratı, list/tuple/string karşılaştırma kuralları, ve sıralama için key fonksiyon pattern&apos;leri.</image:caption>
      <image:title>Karşılaştırma Operatörleri ve Sortable Class: __eq__, __lt__ ve total_ordering Sırrı</image:title>
    </image:image>
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    <loc>https://sukruyusufkaya.com/en/learn/python-programlama/python-karsilastirma-operatorleri-total-ordering</loc>
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    <changefreq>monthly</changefreq>
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    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>&lt; &gt; == != &lt;= &gt;= görünüşte basit ama Python&apos;da chained comparison (`0 &lt; x &lt; 10`), her tip için custom karşılaştırma, ve `@total_ordering` decorator gibi süslü özellikler var. Bu derste: 6 karşılaştırma operatörü derinlemesine, custom sortable class yapımı, __hash__ ve __eq__ kontratı, list/tuple/string karşılaştırma kuralları, ve sıralama için key fonksiyon pattern&apos;leri.</image:caption>
      <image:title>Karşılaştırma Operatörleri ve Sortable Class: __eq__, __lt__ ve total_ordering Sırrı</image:title>
    </image:image>
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    <loc>https://sukruyusufkaya.com/learn/python-programlama/python-mantiksal-operatorler-and-or-not</loc>
    <lastmod>2026-05-10T14:44:11.505Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
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      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>and, or, not görünüşte ilkokul mantığı ama Python&apos;un short-circuit semantiği ile zarif validator&apos;lar, default chain&apos;leri, lazy evaluation pattern&apos;leri yazabiliyorsun. Bu derste: De Morgan kanunları kod ile, any() ve all() built-in&apos;leri, conditional expression, ve gerçek dünya validator + permission check örnekleri.</image:caption>
      <image:title>Mantıksal Operatörler: and, or, not — Short-circuit&apos;ün Pythonic Sanatı</image:title>
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    <loc>https://sukruyusufkaya.com/en/learn/python-programlama/python-mantiksal-operatorler-and-or-not</loc>
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    <priority>0.60</priority>
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      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>and, or, not görünüşte ilkokul mantığı ama Python&apos;un short-circuit semantiği ile zarif validator&apos;lar, default chain&apos;leri, lazy evaluation pattern&apos;leri yazabiliyorsun. Bu derste: De Morgan kanunları kod ile, any() ve all() built-in&apos;leri, conditional expression, ve gerçek dünya validator + permission check örnekleri.</image:caption>
      <image:title>Mantıksal Operatörler: and, or, not — Short-circuit&apos;ün Pythonic Sanatı</image:title>
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    <loc>https://sukruyusufkaya.com/learn/python-programlama/python-bit-level-operatorler</loc>
    <lastmod>2026-05-10T14:44:11.669Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/python-programlama/python-bit-level-operatorler"/>
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    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>AI yapacağım, bit-level lazım mı? Doğrudan değil — ama Linux dosya izinleri (chmod 755), RGB color hex (0xFF8000), network protokolleri, IntFlag enum, kompakt veri yapıları — hepsi bit operatörü kullanıyor. Bu derste: 6 bit operatörü, bit manipulation pattern&apos;leri, IntFlag modern alternatif, ve günlük programcılıkta nerelerde kullanılacağı.</image:caption>
      <image:title>Bit-level Operatörler: Permission Flags, RGB Manipülasyon ve Düşük-Seviye Hızın Dünyası</image:title>
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    <loc>https://sukruyusufkaya.com/en/learn/python-programlama/python-bit-level-operatorler</loc>
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    <priority>0.60</priority>
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    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>AI yapacağım, bit-level lazım mı? Doğrudan değil — ama Linux dosya izinleri (chmod 755), RGB color hex (0xFF8000), network protokolleri, IntFlag enum, kompakt veri yapıları — hepsi bit operatörü kullanıyor. Bu derste: 6 bit operatörü, bit manipulation pattern&apos;leri, IntFlag modern alternatif, ve günlük programcılıkta nerelerde kullanılacağı.</image:caption>
      <image:title>Bit-level Operatörler: Permission Flags, RGB Manipülasyon ve Düşük-Seviye Hızın Dünyası</image:title>
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    <loc>https://sukruyusufkaya.com/learn/python-programlama/python-operator-precedence-assosiyatiflik</loc>
    <lastmod>2026-05-10T15:04:18.929Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
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    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>&apos;a or b and c&apos; nasıl evaluate edilir? &apos;5 + 3 * 2&apos; neden 11 değil 16? &apos;~5 &lt;&lt; 2&apos; nedir? Python&apos;un 18 seviyeli precedence tablosu ve sol/sağ assosiyatiflik kuralları. Bu derste: tam precedence tablosu, klasik tuzaklar, IDE warning&apos;lerinin neden olduğu davranışları, ve &apos;parantez ne zaman gerek&apos; net kararı.</image:caption>
      <image:title>Operator Precedence ve Assosiyatiflik: Parantezsiz Doğru Kod Yazma Sanatı</image:title>
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    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>&apos;a or b and c&apos; nasıl evaluate edilir? &apos;5 + 3 * 2&apos; neden 11 değil 16? &apos;~5 &lt;&lt; 2&apos; nedir? Python&apos;un 18 seviyeli precedence tablosu ve sol/sağ assosiyatiflik kuralları. Bu derste: tam precedence tablosu, klasik tuzaklar, IDE warning&apos;lerinin neden olduğu davranışları, ve &apos;parantez ne zaman gerek&apos; net kararı.</image:caption>
      <image:title>Operator Precedence ve Assosiyatiflik: Parantezsiz Doğru Kod Yazma Sanatı</image:title>
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    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>Python&apos;da bir değeri başka tipe dönüştürmek genelde tek satır: int(&apos;42&apos;), float(3), str(123). Ama detaylar var: __int__/__float__/__str__ magic methods, hangi dönüşüm hata atar, NumPy/pandas dtype&apos;ları, datetime parsing, JSON serialization. Bu derste &apos;cast&apos;ın 8 yaygın senaryosu, custom class&apos;lar için cast desteği, ve Pydantic gibi modern data validation kütüphanelerinin nasıl çalıştığı.</image:caption>
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    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>Python&apos;da bir değeri başka tipe dönüştürmek genelde tek satır: int(&apos;42&apos;), float(3), str(123). Ama detaylar var: __int__/__float__/__str__ magic methods, hangi dönüşüm hata atar, NumPy/pandas dtype&apos;ları, datetime parsing, JSON serialization. Bu derste &apos;cast&apos;ın 8 yaygın senaryosu, custom class&apos;lar için cast desteği, ve Pydantic gibi modern data validation kütüphanelerinin nasıl çalıştığı.</image:caption>
      <image:title>Type Conversion: int, float, str, bool, list, dict, set, bytes — Cast&apos;in 8 Yüzü</image:title>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/python-programlama/python-id-is-eq-identity-vs-equality"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/python-programlama/python-id-is-eq-identity-vs-equality"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>Modül 2&apos;nin capstone&apos;u. `is` ve `==` arasındaki fark — yıllar içinde gördüğüm Python interview sorularının %50&apos;si bunun üzerinde. Bu derste: id() fonksiyonu, identity vs equality kontratı, small int caching ve string interning&apos;in derinlemesine implementasyon detayları, weakref kavramı, ve ne zaman is — ne zaman == kararı.</image:caption>
      <image:title>id(), is, == — Identity vs Equality: Python Bellek Modelinin Final Sınavı</image:title>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/python-programlama/python-id-is-eq-identity-vs-equality</loc>
    <lastmod>2026-05-10T16:24:07.076Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/python-programlama/python-id-is-eq-identity-vs-equality"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/python-programlama/python-id-is-eq-identity-vs-equality"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/python-programlama/python-id-is-eq-identity-vs-equality"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1200&amp;q=80</image:loc>
      <image:caption>Modül 2&apos;nin capstone&apos;u. `is` ve `==` arasındaki fark — yıllar içinde gördüğüm Python interview sorularının %50&apos;si bunun üzerinde. Bu derste: id() fonksiyonu, identity vs equality kontratı, small int caching ve string interning&apos;in derinlemesine implementasyon detayları, weakref kavramı, ve ne zaman is — ne zaman == kararı.</image:caption>
      <image:title>id(), is, == — Identity vs Equality: Python Bellek Modelinin Final Sınavı</image:title>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/chatgpt-nedir-tarihce-evrim</loc>
    <lastmod>2026-05-13T18:34:35.673Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/chatgpt-nedir-tarihce-evrim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/chatgpt-nedir-tarihce-evrim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/chatgpt-nedir-tarihce-evrim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GPT modellerinin doğuşu, ChatGPT&apos;nin Kasım 2022 lansmanı ve 2026&apos;ya kadar olan evrimi. Neden bu kadar büyük bir teknoloji devrimi?</image:caption>
      <image:title>ChatGPT Nedir? Tarihçe, Evrim ve Bugünün Manzarası</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/chatgpt-nedir-tarihce-evrim</loc>
    <lastmod>2026-05-13T18:34:35.673Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/chatgpt-nedir-tarihce-evrim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/chatgpt-nedir-tarihce-evrim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/chatgpt-nedir-tarihce-evrim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GPT modellerinin doğuşu, ChatGPT&apos;nin Kasım 2022 lansmanı ve 2026&apos;ya kadar olan evrimi. Neden bu kadar büyük bir teknoloji devrimi?</image:caption>
      <image:title>ChatGPT Nedir? Tarihçe, Evrim ve Bugünün Manzarası</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/hesap-acma-plan-karsilastirma</loc>
    <lastmod>2026-05-11T14:52:53.808Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/hesap-acma-plan-karsilastirma"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/hesap-acma-plan-karsilastirma"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/hesap-acma-plan-karsilastirma"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Hangi plan sana uygun? Beş ChatGPT planının limitlerini, fiyatlarını ve özelliklerini karşılaştır. Adım adım hesap açma rehberi.</image:caption>
      <image:title>Hesap Açma ve Plan Karşılaştırması: Free, Plus, Pro, Team, Enterprise</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/hesap-acma-plan-karsilastirma</loc>
    <lastmod>2026-05-11T14:52:53.808Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/hesap-acma-plan-karsilastirma"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/hesap-acma-plan-karsilastirma"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/hesap-acma-plan-karsilastirma"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Hangi plan sana uygun? Beş ChatGPT planının limitlerini, fiyatlarını ve özelliklerini karşılaştır. Adım adım hesap açma rehberi.</image:caption>
      <image:title>Hesap Açma ve Plan Karşılaştırması: Free, Plus, Pro, Team, Enterprise</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/arayuz-anatomisi</loc>
    <lastmod>2026-05-11T14:53:42.598Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/arayuz-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/arayuz-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/arayuz-anatomisi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT arayüzünün her bir bileşenini gör: sol panel, sohbet alanı, model seçici, araçlar, ayarlar paneli, kişisel kütüphane.</image:caption>
      <image:title>Arayüz Anatomisi: Her Buton, Menü ve Ayar Açıklamalı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/arayuz-anatomisi</loc>
    <lastmod>2026-05-11T14:53:42.598Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/arayuz-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/arayuz-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/arayuz-anatomisi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT arayüzünün her bir bileşenini gör: sol panel, sohbet alanı, model seçici, araçlar, ayarlar paneli, kişisel kütüphane.</image:caption>
      <image:title>Arayüz Anatomisi: Her Buton, Menü ve Ayar Açıklamalı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ilk-konusma-pratik-tur</loc>
    <lastmod>2026-05-11T14:53:53.965Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ilk-konusma-pratik-tur"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/ilk-konusma-pratik-tur"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ilk-konusma-pratik-tur"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>İlk mesajını gönderip yanıtı alırken neler oluyor? Net bir görev seçimi, prompt, takip soruları, mesaj düzenleme ve kayıt — uygulamalı.</image:caption>
      <image:title>İlk Konuşmanız: Adım Adım Pratik Tur</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/ilk-konusma-pratik-tur</loc>
    <lastmod>2026-05-11T14:53:53.965Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ilk-konusma-pratik-tur"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/ilk-konusma-pratik-tur"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ilk-konusma-pratik-tur"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>İlk mesajını gönderip yanıtı alırken neler oluyor? Net bir görev seçimi, prompt, takip soruları, mesaj düzenleme ve kayıt — uygulamalı.</image:caption>
      <image:title>İlk Konuşmanız: Adım Adım Pratik Tur</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/mobil-masaustu-sesli-mod</loc>
    <lastmod>2026-05-11T13:48:56.559Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/mobil-masaustu-sesli-mod"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/mobil-masaustu-sesli-mod"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/mobil-masaustu-sesli-mod"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>iOS, Android, macOS, Windows uygulamalarının özellikleri. Sesli modun (Standard ve Advanced) kullanımı, dil ayarları, sınırlamalar.</image:caption>
      <image:title>Mobil, Masaüstü ve Sesli Mod — Her Yerde ChatGPT</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/mobil-masaustu-sesli-mod</loc>
    <lastmod>2026-05-11T13:48:56.559Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/mobil-masaustu-sesli-mod"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/mobil-masaustu-sesli-mod"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/mobil-masaustu-sesli-mod"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>iOS, Android, macOS, Windows uygulamalarının özellikleri. Sesli modun (Standard ve Advanced) kullanımı, dil ayarları, sınırlamalar.</image:caption>
      <image:title>Mobil, Masaüstü ve Sesli Mod — Her Yerde ChatGPT</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gecmis-klasorler-projeler</loc>
    <lastmod>2026-05-11T13:48:56.745Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gecmis-klasorler-projeler"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/gecmis-klasorler-projeler"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gecmis-klasorler-projeler"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sohbetleri organize etme: arama, arşivleme, projeler, paylaşılan workspace. Verinin cihazlar arası senkronu nasıl çalışır?</image:caption>
      <image:title>Geçmiş, Klasörler, Projeler ve Senkronizasyon</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/gecmis-klasorler-projeler</loc>
    <lastmod>2026-05-11T13:48:56.745Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gecmis-klasorler-projeler"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/gecmis-klasorler-projeler"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gecmis-klasorler-projeler"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sohbetleri organize etme: arama, arşivleme, projeler, paylaşılan workspace. Verinin cihazlar arası senkronu nasıl çalışır?</image:caption>
      <image:title>Geçmiş, Klasörler, Projeler ve Senkronizasyon</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/prompt-anatomisi</loc>
    <lastmod>2026-05-11T13:48:57.043Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/prompt-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/prompt-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/prompt-anatomisi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Prompt&apos;un dört yapı taşını (bağlam, görev, format, kısıtlar) öğren ve hemen uygula. Her parça yanıt kalitesini nasıl değiştirir?</image:caption>
      <image:title>Prompt Nedir? Anatomi: Bağlam, Görev, Format, Kısıtlar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/prompt-anatomisi</loc>
    <lastmod>2026-05-11T13:48:57.043Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/prompt-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/prompt-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/prompt-anatomisi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Prompt&apos;un dört yapı taşını (bağlam, görev, format, kısıtlar) öğren ve hemen uygula. Her parça yanıt kalitesini nasıl değiştirir?</image:caption>
      <image:title>Prompt Nedir? Anatomi: Bağlam, Görev, Format, Kısıtlar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/iyi-vs-kotu-prompt</loc>
    <lastmod>2026-05-11T13:48:57.267Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/iyi-vs-kotu-prompt"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/iyi-vs-kotu-prompt"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/iyi-vs-kotu-prompt"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Pratikten çıkmış 15 örnek üzerinden iyi prompt ile kötü prompt&apos;un yanıt kalitesindeki farkını gör.</image:caption>
      <image:title>İyi vs Kötü Prompt: 15 Yan Yana Karşılaştırma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/iyi-vs-kotu-prompt</loc>
    <lastmod>2026-05-11T13:48:57.267Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/iyi-vs-kotu-prompt"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/iyi-vs-kotu-prompt"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/iyi-vs-kotu-prompt"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Pratikten çıkmış 15 örnek üzerinden iyi prompt ile kötü prompt&apos;un yanıt kalitesindeki farkını gör.</image:caption>
      <image:title>İyi vs Kötü Prompt: 15 Yan Yana Karşılaştırma</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/aciklik-baglam-spesifiklik</loc>
    <lastmod>2026-05-11T13:48:57.446Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/aciklik-baglam-spesifiklik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/aciklik-baglam-spesifiklik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/aciklik-baglam-spesifiklik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>İyi prompt&apos;un üç temel niteliği: belirsizliği yok et, bağlamı zenginleştir, spesifik ol. Pratik örneklerle.</image:caption>
      <image:title>Açıklık, Bağlam ve Spesifiklik İlkesi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/aciklik-baglam-spesifiklik</loc>
    <lastmod>2026-05-11T13:48:57.446Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/aciklik-baglam-spesifiklik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/aciklik-baglam-spesifiklik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/aciklik-baglam-spesifiklik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>İyi prompt&apos;un üç temel niteliği: belirsizliği yok et, bağlamı zenginleştir, spesifik ol. Pratik örneklerle.</image:caption>
      <image:title>Açıklık, Bağlam ve Spesifiklik İlkesi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/format-komutlari</loc>
    <lastmod>2026-05-11T13:48:57.758Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/format-komutlari"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/format-komutlari"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/format-komutlari"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Çıktıyı tam istediğin gibi yapılandırma sanatı. 5 format için somut örnekler ve şablonlar.</image:caption>
      <image:title>Format Komutları: Liste, Tablo, JSON, Markdown, CSV</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/format-komutlari</loc>
    <lastmod>2026-05-11T13:48:57.758Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/format-komutlari"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/format-komutlari"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/format-komutlari"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Çıktıyı tam istediğin gibi yapılandırma sanatı. 5 format için somut örnekler ve şablonlar.</image:caption>
      <image:title>Format Komutları: Liste, Tablo, JSON, Markdown, CSV</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/rol-atama</loc>
    <lastmod>2026-05-11T13:48:58.043Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/rol-atama"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/rol-atama"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/rol-atama"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modele rol vererek yanıt kalitesini sıçratma tekniği. 20 hazır rol şablonu ve hangi senaryoda hangisi.</image:caption>
      <image:title>Rol Atama (Role Prompting): &apos;Sen bir X uzmanısın...&apos;</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/rol-atama</loc>
    <lastmod>2026-05-11T13:48:58.043Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/rol-atama"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/rol-atama"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/rol-atama"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modele rol vererek yanıt kalitesini sıçratma tekniği. 20 hazır rol şablonu ve hangi senaryoda hangisi.</image:caption>
      <image:title>Rol Atama (Role Prompting): &apos;Sen bir X uzmanısın...&apos;</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ton-ve-stil-kontrolu</loc>
    <lastmod>2026-05-11T13:48:58.223Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ton-ve-stil-kontrolu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/ton-ve-stil-kontrolu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ton-ve-stil-kontrolu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Aynı içeriği 5 farklı tonda yazdırma teknikleri. Yazı ses tonunu nasıl matematik gibi kontrol edersin?</image:caption>
      <image:title>Ton ve Stil Kontrolü: Resmi, Samimi, Akademik, Eğlenceli</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/ton-ve-stil-kontrolu</loc>
    <lastmod>2026-05-11T13:48:58.223Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ton-ve-stil-kontrolu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/ton-ve-stil-kontrolu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ton-ve-stil-kontrolu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Aynı içeriği 5 farklı tonda yazdırma teknikleri. Yazı ses tonunu nasıl matematik gibi kontrol edersin?</image:caption>
      <image:title>Ton ve Stil Kontrolü: Resmi, Samimi, Akademik, Eğlenceli</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/turkce-prompt-hatalari</loc>
    <lastmod>2026-05-11T13:48:58.449Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/turkce-prompt-hatalari"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/turkce-prompt-hatalari"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/turkce-prompt-hatalari"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Türkçe yazarken model&apos;in kafasını karıştıran yaygın 10 hata ve net çözümleri.</image:caption>
      <image:title>Türkçe Promptlamada Sık Yapılan Hatalar (ve Düzeltmeleri)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/turkce-prompt-hatalari</loc>
    <lastmod>2026-05-11T13:48:58.449Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/turkce-prompt-hatalari"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/turkce-prompt-hatalari"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/turkce-prompt-hatalari"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Türkçe yazarken model&apos;in kafasını karıştıran yaygın 10 hata ve net çözümleri.</image:caption>
      <image:title>Türkçe Promptlamada Sık Yapılan Hatalar (ve Düzeltmeleri)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/few-shot-learning</loc>
    <lastmod>2026-05-11T13:48:58.790Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/few-shot-learning"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/few-shot-learning"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/few-shot-learning"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modele birkaç örnek vererek herhangi bir görevi sıfır eğitimle yaptırma sanatı. Pattern matching gücünü kullanma.</image:caption>
      <image:title>Few-Shot Learning: Örneklerle Öğretmek</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/few-shot-learning</loc>
    <lastmod>2026-05-11T13:48:58.790Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/few-shot-learning"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/few-shot-learning"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/few-shot-learning"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modele birkaç örnek vererek herhangi bir görevi sıfır eğitimle yaptırma sanatı. Pattern matching gücünü kullanma.</image:caption>
      <image:title>Few-Shot Learning: Örneklerle Öğretmek</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/chain-of-thought</loc>
    <lastmod>2026-05-11T13:48:59.066Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/chain-of-thought"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/chain-of-thought"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/chain-of-thought"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modeli cevap vermeden önce &apos;düşündürmek&apos; bir trick mi, yoksa keşif mi? CoT&apos;un karmaşık görevlerde nasıl 30%+ doğruluk getirdiği.</image:caption>
      <image:title>Chain-of-Thought: Adım Adım Düşündürme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/chain-of-thought</loc>
    <lastmod>2026-05-11T13:48:59.066Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/chain-of-thought"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/chain-of-thought"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/chain-of-thought"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modeli cevap vermeden önce &apos;düşündürmek&apos; bir trick mi, yoksa keşif mi? CoT&apos;un karmaşık görevlerde nasıl 30%+ doğruluk getirdiği.</image:caption>
      <image:title>Chain-of-Thought: Adım Adım Düşündürme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/self-consistency</loc>
    <lastmod>2026-05-11T13:48:59.300Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/self-consistency"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/self-consistency"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/self-consistency"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Aynı soruya birden fazla farklı yoldan cevap üretip &apos;oy çokluğunu&apos; alma. Doğruluk için en güçlü tekniklerden.</image:caption>
      <image:title>Self-Consistency: Çoklu Yol ile Doğrulama</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/self-consistency</loc>
    <lastmod>2026-05-11T13:48:59.300Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/self-consistency"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/self-consistency"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/self-consistency"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Aynı soruya birden fazla farklı yoldan cevap üretip &apos;oy çokluğunu&apos; alma. Doğruluk için en güçlü tekniklerden.</image:caption>
      <image:title>Self-Consistency: Çoklu Yol ile Doğrulama</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/tree-of-thoughts</loc>
    <lastmod>2026-05-11T13:48:59.525Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/tree-of-thoughts"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/tree-of-thoughts"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/tree-of-thoughts"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Linear CoT yerine &apos;düşünce ağacı&apos; — model alternatif yolları aynı anda keşfeder, en iyiyi seçer. Karmaşık planlama için.</image:caption>
      <image:title>Tree of Thoughts (ToT): Dallanmalı Akıl Yürütme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/tree-of-thoughts</loc>
    <lastmod>2026-05-11T13:48:59.525Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/tree-of-thoughts"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/tree-of-thoughts"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/tree-of-thoughts"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Linear CoT yerine &apos;düşünce ağacı&apos; — model alternatif yolları aynı anda keşfeder, en iyiyi seçer. Karmaşık planlama için.</image:caption>
      <image:title>Tree of Thoughts (ToT): Dallanmalı Akıl Yürütme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/react-pattern</loc>
    <lastmod>2026-05-11T13:48:59.721Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/react-pattern"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/react-pattern"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/react-pattern"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modeli düşünme + harekete geçme döngüsünde tutmak. Web araması, hesaplama, API çağrılarıyla zincirleme akıl yürütmenin temeli.</image:caption>
      <image:title>ReAct Pattern: Reasoning + Acting Döngüsü</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/react-pattern</loc>
    <lastmod>2026-05-11T13:48:59.721Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/react-pattern"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/react-pattern"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/react-pattern"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modeli düşünme + harekete geçme döngüsünde tutmak. Web araması, hesaplama, API çağrılarıyla zincirleme akıl yürütmenin temeli.</image:caption>
      <image:title>ReAct Pattern: Reasoning + Acting Döngüsü</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/meta-prompting</loc>
    <lastmod>2026-05-11T13:48:59.921Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/meta-prompting"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/meta-prompting"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/meta-prompting"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Promptu kendi yazma yerine modelden iyi prompt isteme tekniği. 10 dakikalık görev için 1 saatlik prompt mühendisliği yapma derdi yok.</image:caption>
      <image:title>Meta-Prompting: ChatGPT&apos;ye &apos;Daha İyi Prompt Yaz&apos; Dedirtmek</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/meta-prompting</loc>
    <lastmod>2026-05-11T13:48:59.921Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/meta-prompting"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/meta-prompting"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/meta-prompting"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Promptu kendi yazma yerine modelden iyi prompt isteme tekniği. 10 dakikalık görev için 1 saatlik prompt mühendisliği yapma derdi yok.</image:caption>
      <image:title>Meta-Prompting: ChatGPT&apos;ye &apos;Daha İyi Prompt Yaz&apos; Dedirtmek</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/negative-prompting</loc>
    <lastmod>2026-05-11T13:49:00.122Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/negative-prompting"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/negative-prompting"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/negative-prompting"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Kötü çıktıları önlemek için neyin **yapılmaması** gerektiğini söylemek. Etkili kullanım kuralları.</image:caption>
      <image:title>Karşıt-Örnekleme (Negative Prompting): &apos;Yapma&apos; Listesi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/negative-prompting</loc>
    <lastmod>2026-05-11T13:49:00.122Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/negative-prompting"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/negative-prompting"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/negative-prompting"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Kötü çıktıları önlemek için neyin **yapılmaması** gerektiğini söylemek. Etkili kullanım kuralları.</image:caption>
      <image:title>Karşıt-Örnekleme (Negative Prompting): &apos;Yapma&apos; Listesi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/iteratif-iyilestirme</loc>
    <lastmod>2026-05-11T13:49:00.319Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/iteratif-iyilestirme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/iteratif-iyilestirme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/iteratif-iyilestirme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tek seferde mükemmel çıktı yok. Üç turda %95 kaliteye nasıl ulaşırsın?</image:caption>
      <image:title>İteratif İyileştirme: Loop Prompting Workflow&apos;u</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/iteratif-iyilestirme</loc>
    <lastmod>2026-05-11T13:49:00.319Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/iteratif-iyilestirme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/iteratif-iyilestirme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/iteratif-iyilestirme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tek seferde mükemmel çıktı yok. Üç turda %95 kaliteye nasıl ulaşırsın?</image:caption>
      <image:title>İteratif İyileştirme: Loop Prompting Workflow&apos;u</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/system-prompts-custom-instructions</loc>
    <lastmod>2026-05-11T13:49:00.517Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/system-prompts-custom-instructions"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/system-prompts-custom-instructions"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/system-prompts-custom-instructions"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Her sohbette tekrarlamak yerine modelin davranışını kalıcı olarak ayarlamak. Custom Instructions ve API&apos;de system prompt.</image:caption>
      <image:title>System Prompts ve Custom Instructions: Kalıcı Davranış Şekillendirme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/system-prompts-custom-instructions</loc>
    <lastmod>2026-05-11T13:49:00.517Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/system-prompts-custom-instructions"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/system-prompts-custom-instructions"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/system-prompts-custom-instructions"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Her sohbette tekrarlamak yerine modelin davranışını kalıcı olarak ayarlamak. Custom Instructions ve API&apos;de system prompt.</image:caption>
      <image:title>System Prompts ve Custom Instructions: Kalıcı Davranış Şekillendirme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/constrained-generation</loc>
    <lastmod>2026-05-11T13:49:00.711Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/constrained-generation"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/constrained-generation"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/constrained-generation"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modeli belirli sınırlar içine almak — JSON modu, regex, max_tokens, stop sequences ve format zorlamaları.</image:caption>
      <image:title>Constrained Generation: Token, Format, Uzunluk Sınırlaması</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/constrained-generation</loc>
    <lastmod>2026-05-11T13:49:00.711Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/constrained-generation"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/constrained-generation"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/constrained-generation"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modeli belirli sınırlar içine almak — JSON modu, regex, max_tokens, stop sequences ve format zorlamaları.</image:caption>
      <image:title>Constrained Generation: Token, Format, Uzunluk Sınırlaması</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/output-parsing</loc>
    <lastmod>2026-05-11T13:49:00.947Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/output-parsing"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/output-parsing"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/output-parsing"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Çıktıyı parse edilebilir hale getirmek. JSON, XML, custom delimiter&apos;lar ve tip-güvenliği.</image:caption>
      <image:title>Output Parsing: JSON Schema, XML Tags, Structured Outputs</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/output-parsing</loc>
    <lastmod>2026-05-11T13:49:00.947Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/output-parsing"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/output-parsing"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/output-parsing"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Çıktıyı parse edilebilir hale getirmek. JSON, XML, custom delimiter&apos;lar ve tip-güvenliği.</image:caption>
      <image:title>Output Parsing: JSON Schema, XML Tags, Structured Outputs</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/multi-step-pipelines</loc>
    <lastmod>2026-05-11T13:49:01.164Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/multi-step-pipelines"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/multi-step-pipelines"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/multi-step-pipelines"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Karmaşık görevi alt görevlere bölüp her birini ayrı LLM çağrısı ile çözme. Hata izolasyonu, kalite ve maliyet avantajları.</image:caption>
      <image:title>Multi-Step Prompting: Pipeline Tasarımı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/multi-step-pipelines</loc>
    <lastmod>2026-05-11T13:49:01.164Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/multi-step-pipelines"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/multi-step-pipelines"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/multi-step-pipelines"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Karmaşık görevi alt görevlere bölüp her birini ayrı LLM çağrısı ile çözme. Hata izolasyonu, kalite ve maliyet avantajları.</image:caption>
      <image:title>Multi-Step Prompting: Pipeline Tasarımı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/prompt-injection</loc>
    <lastmod>2026-05-11T13:49:01.364Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/prompt-injection"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/prompt-injection"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/prompt-injection"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Kullanıcı girdisinin sistem promptunu nasıl ele geçirdiği. 5 saldırı türü ve 7 savunma katmanı.</image:caption>
      <image:title>Prompt Injection: Saldırılar ve Savunma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/prompt-injection</loc>
    <lastmod>2026-05-11T13:49:01.364Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/prompt-injection"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/prompt-injection"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/prompt-injection"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Kullanıcı girdisinin sistem promptunu nasıl ele geçirdiği. 5 saldırı türü ve 7 savunma katmanı.</image:caption>
      <image:title>Prompt Injection: Saldırılar ve Savunma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/prompt-versioning-ab-testing</loc>
    <lastmod>2026-05-11T13:49:01.560Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/prompt-versioning-ab-testing"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/prompt-versioning-ab-testing"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/prompt-versioning-ab-testing"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Üretimdeki promptları yazılım kodu gibi yönetmek. Sürüm kontrolü, A/B testleri, performans ölçümü.</image:caption>
      <image:title>Prompt Versioning ve A/B Testing</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/prompt-versioning-ab-testing</loc>
    <lastmod>2026-05-11T13:49:01.560Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/prompt-versioning-ab-testing"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/prompt-versioning-ab-testing"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/prompt-versioning-ab-testing"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Üretimdeki promptları yazılım kodu gibi yönetmek. Sürüm kontrolü, A/B testleri, performans ölçümü.</image:caption>
      <image:title>Prompt Versioning ve A/B Testing</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/blog-yazilari-workflow</loc>
    <lastmod>2026-05-11T13:49:01.719Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/blog-yazilari-workflow"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/blog-yazilari-workflow"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/blog-yazilari-workflow"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551434678-e076c223a692?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Niş seçimi, anahtar kelime analizi, outline, taslak, revizyon, SEO ve yayın. ChatGPT ile 800-1500 kelimelik blog yazısı 1 saatte.</image:caption>
      <image:title>Blog Yazıları: Outline&apos;dan Yayına Tam Workflow</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/blog-yazilari-workflow</loc>
    <lastmod>2026-05-11T13:49:01.719Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/blog-yazilari-workflow"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/blog-yazilari-workflow"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/blog-yazilari-workflow"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551434678-e076c223a692?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Niş seçimi, anahtar kelime analizi, outline, taslak, revizyon, SEO ve yayın. ChatGPT ile 800-1500 kelimelik blog yazısı 1 saatte.</image:caption>
      <image:title>Blog Yazıları: Outline&apos;dan Yayına Tam Workflow</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/e-posta-sablonlari</loc>
    <lastmod>2026-05-11T13:49:01.922Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/e-posta-sablonlari"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/e-posta-sablonlari"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/e-posta-sablonlari"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>20 hazır e-posta şablonu — ChatGPT ile günlük yazışmaları 5 dakikaya indirme.</image:caption>
      <image:title>E-posta: Soğuk Erişim, Takip, Şikayet, Teşekkür Şablonları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/e-posta-sablonlari</loc>
    <lastmod>2026-05-11T13:49:01.922Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/e-posta-sablonlari"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/e-posta-sablonlari"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/e-posta-sablonlari"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>20 hazır e-posta şablonu — ChatGPT ile günlük yazışmaları 5 dakikaya indirme.</image:caption>
      <image:title>E-posta: Soğuk Erişim, Takip, Şikayet, Teşekkür Şablonları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sosyal-medya-format-spesifik</loc>
    <lastmod>2026-05-13T18:25:23.262Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sosyal-medya-format-spesifik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sosyal-medya-format-spesifik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sosyal-medya-format-spesifik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Her platformun kendine özgü kuralları ve algoritma davranışı. ChatGPT ile platform-uyarlamalı içerik üretimi.</image:caption>
      <image:title>Sosyal Medya: LinkedIn, X, Instagram, TikTok için Format-Spesifik Üretim</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sosyal-medya-format-spesifik</loc>
    <lastmod>2026-05-13T18:25:23.262Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sosyal-medya-format-spesifik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sosyal-medya-format-spesifik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sosyal-medya-format-spesifik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Her platformun kendine özgü kuralları ve algoritma davranışı. ChatGPT ile platform-uyarlamalı içerik üretimi.</image:caption>
      <image:title>Sosyal Medya: LinkedIn, X, Instagram, TikTok için Format-Spesifik Üretim</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/copywriting-cerceveler</loc>
    <lastmod>2026-05-11T13:49:02.326Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/copywriting-cerceveler"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/copywriting-cerceveler"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/copywriting-cerceveler"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>3 klasik copywriting çerçevesi ve ChatGPT ile her birinin promptu. Reklam, landing page, ürün açıklaması.</image:caption>
      <image:title>Pazarlama Metinleri (Copywriting): AIDA, PAS, FAB Çerçeveleri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/copywriting-cerceveler</loc>
    <lastmod>2026-05-11T13:49:02.326Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/copywriting-cerceveler"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/copywriting-cerceveler"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/copywriting-cerceveler"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>3 klasik copywriting çerçevesi ve ChatGPT ile her birinin promptu. Reklam, landing page, ürün açıklaması.</image:caption>
      <image:title>Pazarlama Metinleri (Copywriting): AIDA, PAS, FAB Çerçeveleri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/seo-icerik-briefing</loc>
    <lastmod>2026-05-11T13:49:02.546Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/seo-icerik-briefing"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/seo-icerik-briefing"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/seo-icerik-briefing"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anahtar kelime araştırmasından yayına: SEO odaklı içerik üretiminin tüm aşamaları + ChatGPT ile her aşama.</image:caption>
      <image:title>SEO İçerik: Keyword Briefing&apos;den Meta Description&apos;a</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/seo-icerik-briefing</loc>
    <lastmod>2026-05-11T13:49:02.546Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/seo-icerik-briefing"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/seo-icerik-briefing"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/seo-icerik-briefing"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anahtar kelime araştırmasından yayına: SEO odaklı içerik üretiminin tüm aşamaları + ChatGPT ile her aşama.</image:caption>
      <image:title>SEO İçerik: Keyword Briefing&apos;den Meta Description&apos;a</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/yaratici-yazarlik</loc>
    <lastmod>2026-05-11T13:49:02.719Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/yaratici-yazarlik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/yaratici-yazarlik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/yaratici-yazarlik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT ile yazar bloğunu kırma, karakter geliştirme, dil oyunları, türler arası deneyler.</image:caption>
      <image:title>Yaratıcı Yazarlık: Hikaye, Şiir, Senaryo, Diyalog</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/yaratici-yazarlik</loc>
    <lastmod>2026-05-11T13:49:02.719Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/yaratici-yazarlik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/yaratici-yazarlik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/yaratici-yazarlik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT ile yazar bloğunu kırma, karakter geliştirme, dil oyunları, türler arası deneyler.</image:caption>
      <image:title>Yaratıcı Yazarlık: Hikaye, Şiir, Senaryo, Diyalog</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/akademik-yazim</loc>
    <lastmod>2026-05-11T13:49:02.923Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/akademik-yazim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/akademik-yazim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/akademik-yazim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tez, makale, sunum için ChatGPT — etik sınırları korunarak akademik yazıma katkı.</image:caption>
      <image:title>Akademik Yazım Asistanlığı: Özet, Literatür, Atıf</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/akademik-yazim</loc>
    <lastmod>2026-05-11T13:49:02.923Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/akademik-yazim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/akademik-yazim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/akademik-yazim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tez, makale, sunum için ChatGPT — etik sınırları korunarak akademik yazıma katkı.</image:caption>
      <image:title>Akademik Yazım Asistanlığı: Özet, Literatür, Atıf</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-interpreter-tanisma</loc>
    <lastmod>2026-05-11T13:49:03.146Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-interpreter-tanisma"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/code-interpreter-tanisma"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-interpreter-tanisma"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;nin Python sandbox&apos;ı: dosya yükle, kod çalıştır, görsel üret. Tüm yetenekler ve sınırlamalar.</image:caption>
      <image:title>Code Interpreter / Advanced Data Analysis: Tanışma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/code-interpreter-tanisma</loc>
    <lastmod>2026-05-11T13:49:03.146Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-interpreter-tanisma"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/code-interpreter-tanisma"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-interpreter-tanisma"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;nin Python sandbox&apos;ı: dosya yükle, kod çalıştır, görsel üret. Tüm yetenekler ve sınırlamalar.</image:caption>
      <image:title>Code Interpreter / Advanced Data Analysis: Tanışma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/csv-excel-kesif</loc>
    <lastmod>2026-05-11T13:49:03.316Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/csv-excel-kesif"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/csv-excel-kesif"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/csv-excel-kesif"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Yeni veri seti elinize geçti — 30 saniyede ne içerdiğini, kalitesini, ön bulgularını öğrenin.</image:caption>
      <image:title>CSV/Excel Yükleme ve Otomatik Keşif Analizi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/csv-excel-kesif</loc>
    <lastmod>2026-05-11T13:49:03.316Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/csv-excel-kesif"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/csv-excel-kesif"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/csv-excel-kesif"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Yeni veri seti elinize geçti — 30 saniyede ne içerdiğini, kalitesini, ön bulgularını öğrenin.</image:caption>
      <image:title>CSV/Excel Yükleme ve Otomatik Keşif Analizi</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/grafik-uretimi</loc>
    <lastmod>2026-05-11T13:49:03.504Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/grafik-uretimi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/grafik-uretimi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/grafik-uretimi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Doğru grafik tipini seçme + ChatGPT promptlarıyla profesyonel görselleştirme.</image:caption>
      <image:title>Grafik Üretimi: Histogram, Scatter, Heatmap, Time Series</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/grafik-uretimi</loc>
    <lastmod>2026-05-11T13:49:03.504Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/grafik-uretimi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/grafik-uretimi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/grafik-uretimi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Doğru grafik tipini seçme + ChatGPT promptlarıyla profesyonel görselleştirme.</image:caption>
      <image:title>Grafik Üretimi: Histogram, Scatter, Heatmap, Time Series</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sql-sorgu-yazimi</loc>
    <lastmod>2026-05-11T13:49:03.687Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sql-sorgu-yazimi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sql-sorgu-yazimi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sql-sorgu-yazimi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT ile doğal dilden SQL&apos;e, mevcut sorguyu açıklama, optimizasyon ve hata bulma.</image:caption>
      <image:title>SQL Sorgu Yazımı ve Açıklama</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sql-sorgu-yazimi</loc>
    <lastmod>2026-05-11T13:49:03.687Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sql-sorgu-yazimi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sql-sorgu-yazimi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sql-sorgu-yazimi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT ile doğal dilden SQL&apos;e, mevcut sorguyu açıklama, optimizasyon ve hata bulma.</image:caption>
      <image:title>SQL Sorgu Yazımı ve Açıklama</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/istatistik-testler</loc>
    <lastmod>2026-05-11T13:49:03.886Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/istatistik-testler"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/istatistik-testler"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/istatistik-testler"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>t-test, chi-square, ANOVA, korelasyon testleri — Code Interpreter ile otomatik ve sonuçların yorumu.</image:caption>
      <image:title>İstatistiksel Testler ve Yorumlama</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/istatistik-testler</loc>
    <lastmod>2026-05-11T13:49:03.886Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/istatistik-testler"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/istatistik-testler"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/istatistik-testler"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>t-test, chi-square, ANOVA, korelasyon testleri — Code Interpreter ile otomatik ve sonuçların yorumu.</image:caption>
      <image:title>İstatistiksel Testler ve Yorumlama</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/veri-temizleme</loc>
    <lastmod>2026-05-11T13:49:04.119Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/veri-temizleme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/veri-temizleme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/veri-temizleme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Pratik 6 adımlı veri temizleme şablonu. Eksik değer, outlier, tip uyumsuzluğu, duplicate, format normalizasyonu.</image:caption>
      <image:title>Veri Temizleme Workflow&apos;u: Eksik, Aykırı, Tip Düzeltme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/veri-temizleme</loc>
    <lastmod>2026-05-11T13:49:04.119Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/veri-temizleme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/veri-temizleme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/veri-temizleme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Pratik 6 adımlı veri temizleme şablonu. Eksik değer, outlier, tip uyumsuzluğu, duplicate, format normalizasyonu.</image:caption>
      <image:title>Veri Temizleme Workflow&apos;u: Eksik, Aykırı, Tip Düzeltme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/spec-to-code</loc>
    <lastmod>2026-05-11T13:49:04.318Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/spec-to-code"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/spec-to-code"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/spec-to-code"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Belirsiz görev fikrinden çalışan, test edilmiş, dokümante koda — pipeline ile profesyonel akış.</image:caption>
      <image:title>Kod Yazma: Spec → Kod Akışı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/spec-to-code</loc>
    <lastmod>2026-05-11T13:49:04.318Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/spec-to-code"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/spec-to-code"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/spec-to-code"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Belirsiz görev fikrinden çalışan, test edilmiş, dokümante koda — pipeline ile profesyonel akış.</image:caption>
      <image:title>Kod Yazma: Spec → Kod Akışı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/debugging</loc>
    <lastmod>2026-05-11T13:49:04.518Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/debugging"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/debugging"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/debugging"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Hata mesajını ChatGPT&apos;ye verirken neyi nasıl ekleyeceksin? &apos;Bu kod çalışmıyor&apos;tan profesyonel debug oturumuna.</image:caption>
      <image:title>Hata Ayıklama: Stack Trace ile Sohbet</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/debugging</loc>
    <lastmod>2026-05-11T13:49:04.518Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/debugging"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/debugging"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/debugging"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Hata mesajını ChatGPT&apos;ye verirken neyi nasıl ekleyeceksin? &apos;Bu kod çalışmıyor&apos;tan profesyonel debug oturumuna.</image:caption>
      <image:title>Hata Ayıklama: Stack Trace ile Sohbet</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-review-refactoring</loc>
    <lastmod>2026-05-11T13:49:04.757Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-review-refactoring"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/code-review-refactoring"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-review-refactoring"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;yi kıdemli mühendis gibi kod inceleme ortağı yap. Smell tespiti, refactor önerileri.</image:caption>
      <image:title>Code Review ve Refactoring İstemleri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/code-review-refactoring</loc>
    <lastmod>2026-05-11T13:49:04.757Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-review-refactoring"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/code-review-refactoring"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-review-refactoring"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;yi kıdemli mühendis gibi kod inceleme ortağı yap. Smell tespiti, refactor önerileri.</image:caption>
      <image:title>Code Review ve Refactoring İstemleri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/unit-test-uretimi</loc>
    <lastmod>2026-05-11T13:49:04.961Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/unit-test-uretimi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/unit-test-uretimi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/unit-test-uretimi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Mevcut kod için kapsamlı test üretmek, edge case&apos;leri yakalamak. 3 popüler framework için promptlar.</image:caption>
      <image:title>Unit Test Üretimi: Jest, Pytest, JUnit Örnekleri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/unit-test-uretimi</loc>
    <lastmod>2026-05-11T13:49:04.961Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/unit-test-uretimi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/unit-test-uretimi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/unit-test-uretimi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Mevcut kod için kapsamlı test üretmek, edge case&apos;leri yakalamak. 3 popüler framework için promptlar.</image:caption>
      <image:title>Unit Test Üretimi: Jest, Pytest, JUnit Örnekleri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/kod-dokumantasyonu</loc>
    <lastmod>2026-05-11T13:49:05.203Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/kod-dokumantasyonu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/kod-dokumantasyonu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/kod-dokumantasyonu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Mevcut kodu dokümante etme: inline doc, README, API docs. ChatGPT ile manuel zaman 10x.</image:caption>
      <image:title>Dokümantasyon: JSDoc, Docstring, README</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/kod-dokumantasyonu</loc>
    <lastmod>2026-05-11T13:49:05.203Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/kod-dokumantasyonu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/kod-dokumantasyonu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/kod-dokumantasyonu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Mevcut kodu dokümante etme: inline doc, README, API docs. ChatGPT ile manuel zaman 10x.</image:caption>
      <image:title>Dokümantasyon: JSDoc, Docstring, README</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-translation</loc>
    <lastmod>2026-05-11T13:49:05.360Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-translation"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/code-translation"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-translation"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir dilin kodunu başka dile çevirme: deyim farkları, idiom hatlama, ekosistem uyumu.</image:caption>
      <image:title>Code Translation: Python ↔ JS ↔ TS ↔ Go ↔ Rust</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/code-translation</loc>
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    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-translation"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/code-translation"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-translation"/>
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      <image:caption>Bir dilin kodunu başka dile çevirme: deyim farkları, idiom hatlama, ekosistem uyumu.</image:caption>
      <image:title>Code Translation: Python ↔ JS ↔ TS ↔ Go ↔ Rust</image:title>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ide-entegrasyonlari</loc>
    <lastmod>2026-05-11T13:49:05.568Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ide-entegrasyonlari"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/ide-entegrasyonlari"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ide-entegrasyonlari"/>
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      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Editor içinde ChatGPT — copilot, inline suggest, agent mode. Üretkenliği 5x katlayan setup.</image:caption>
      <image:title>IDE Entegrasyonları: VS Code, Cursor, JetBrains</image:title>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/ide-entegrasyonlari</loc>
    <lastmod>2026-05-11T13:49:05.568Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ide-entegrasyonlari"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/ide-entegrasyonlari"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ide-entegrasyonlari"/>
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      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Editor içinde ChatGPT — copilot, inline suggest, agent mode. Üretkenliği 5x katlayan setup.</image:caption>
      <image:title>IDE Entegrasyonları: VS Code, Cursor, JetBrains</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gorsel-analiz</loc>
    <lastmod>2026-05-11T13:49:05.744Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gorsel-analiz"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/gorsel-analiz"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gorsel-analiz"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;ye fotoğraf yükle, sorularını sor — günlük 50 use case ile pratik tur.</image:caption>
      <image:title>Görsel Analiz: Fotoğraf, Belge, Ekran Görüntüsü Anlama</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/gorsel-analiz</loc>
    <lastmod>2026-05-11T13:49:05.744Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gorsel-analiz"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/gorsel-analiz"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gorsel-analiz"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;ye fotoğraf yükle, sorularını sor — günlük 50 use case ile pratik tur.</image:caption>
      <image:title>Görsel Analiz: Fotoğraf, Belge, Ekran Görüntüsü Anlama</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/diyagram-tablo-grafik-okuma</loc>
    <lastmod>2026-05-11T13:49:05.937Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/diyagram-tablo-grafik-okuma"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/diyagram-tablo-grafik-okuma"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/diyagram-tablo-grafik-okuma"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Mermaid, Excel tablosu, finansal grafik — yapılandırılmış görselleri ChatGPT ile okuma sanatı.</image:caption>
      <image:title>Diyagram, Tablo, Grafik Okuma</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/diyagram-tablo-grafik-okuma</loc>
    <lastmod>2026-05-11T13:49:05.937Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/diyagram-tablo-grafik-okuma"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/diyagram-tablo-grafik-okuma"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/diyagram-tablo-grafik-okuma"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Mermaid, Excel tablosu, finansal grafik — yapılandırılmış görselleri ChatGPT ile okuma sanatı.</image:caption>
      <image:title>Diyagram, Tablo, Grafik Okuma</image:title>
    </image:image>
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  <url>
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    <lastmod>2026-05-11T13:49:06.116Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ui-debug-screenshot"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/ui-debug-screenshot"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ui-debug-screenshot"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bozuk butona, kayık layout&apos;a, mobile breakpoint sorununa — ekran görüntüsü ile profesyonel debug.</image:caption>
      <image:title>UI Debug: Ekran Görüntüsü ile CSS/Layout Sorun Çözme</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/ui-debug-screenshot</loc>
    <lastmod>2026-05-11T13:49:06.116Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ui-debug-screenshot"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/ui-debug-screenshot"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ui-debug-screenshot"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bozuk butona, kayık layout&apos;a, mobile breakpoint sorununa — ekran görüntüsü ile profesyonel debug.</image:caption>
      <image:title>UI Debug: Ekran Görüntüsü ile CSS/Layout Sorun Çözme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/dall-e-gorsel-uretimi</loc>
    <lastmod>2026-05-11T13:49:06.320Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/dall-e-gorsel-uretimi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/dall-e-gorsel-uretimi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/dall-e-gorsel-uretimi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT içinden DALL-E 3 ile görsel üretme. Prompt mimarisi, stil, kompozisyon, format.</image:caption>
      <image:title>DALL-E ile Görsel Üretimi: Prompt Mimarisi</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/dall-e-gorsel-uretimi</loc>
    <lastmod>2026-05-11T13:49:06.320Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/dall-e-gorsel-uretimi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/dall-e-gorsel-uretimi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/dall-e-gorsel-uretimi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT içinden DALL-E 3 ile görsel üretme. Prompt mimarisi, stil, kompozisyon, format.</image:caption>
      <image:title>DALL-E ile Görsel Üretimi: Prompt Mimarisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sesli-mod-detayli</loc>
    <lastmod>2026-05-11T13:49:06.480Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sesli-mod-detayli"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sesli-mod-detayli"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sesli-mod-detayli"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Yürürken brainstorm, dil pratiği, çeviri asistanlığı, accessibility — sesli modun günlük 12 use case&apos;i.</image:caption>
      <image:title>Sesli Mod (Standard ve Advanced): Detaylı Kullanım</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sesli-mod-detayli</loc>
    <lastmod>2026-05-11T13:49:06.480Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sesli-mod-detayli"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sesli-mod-detayli"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sesli-mod-detayli"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Yürürken brainstorm, dil pratiği, çeviri asistanlığı, accessibility — sesli modun günlük 12 use case&apos;i.</image:caption>
      <image:title>Sesli Mod (Standard ve Advanced): Detaylı Kullanım</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/video-anlama-sora</loc>
    <lastmod>2026-05-11T13:49:06.670Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/video-anlama-sora"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/video-anlama-sora"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/video-anlama-sora"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sora ile video üretimi, ChatGPT&apos;ye video yükleme, frame analizi. 2026&apos;nın çok modlu sınırı.</image:caption>
      <image:title>Video Anlama (Sora ve Yeni Yetenekler) — 2026 Güncellemesi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/video-anlama-sora</loc>
    <lastmod>2026-05-11T13:49:06.670Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/video-anlama-sora"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/video-anlama-sora"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/video-anlama-sora"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sora ile video üretimi, ChatGPT&apos;ye video yükleme, frame analizi. 2026&apos;nın çok modlu sınırı.</image:caption>
      <image:title>Video Anlama (Sora ve Yeni Yetenekler) — 2026 Güncellemesi</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/custom-gpt-anatomisi</loc>
    <lastmod>2026-05-11T13:49:06.881Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/custom-gpt-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/custom-gpt-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/custom-gpt-anatomisi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Custom GPT&apos;nin 4 temel parçası ve her birinin ne işe yaradığı.</image:caption>
      <image:title>Custom GPT Anatomisi: Instructions, Knowledge, Capabilities, Actions</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/custom-gpt-anatomisi</loc>
    <lastmod>2026-05-11T13:49:06.881Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/custom-gpt-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/custom-gpt-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/custom-gpt-anatomisi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Custom GPT&apos;nin 4 temel parçası ve her birinin ne işe yaradığı.</image:caption>
      <image:title>Custom GPT Anatomisi: Instructions, Knowledge, Capabilities, Actions</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gpt-builder</loc>
    <lastmod>2026-05-11T13:49:07.101Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gpt-builder"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/gpt-builder"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gpt-builder"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Adım adım GPT Builder kullanımı: ad, açıklama, talimat, knowledge, conversation starters, profil görseli.</image:caption>
      <image:title>GPT Builder ile Konuşarak GPT Oluşturma</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/gpt-builder</loc>
    <lastmod>2026-05-11T13:49:07.101Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gpt-builder"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/gpt-builder"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gpt-builder"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Adım adım GPT Builder kullanımı: ad, açıklama, talimat, knowledge, conversation starters, profil görseli.</image:caption>
      <image:title>GPT Builder ile Konuşarak GPT Oluşturma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/knowledge-files-rag</loc>
    <lastmod>2026-05-11T13:49:07.283Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/knowledge-files-rag"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/knowledge-files-rag"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/knowledge-files-rag"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Yüklediğin dokümanın &apos;akıllı&apos; bulunabilirliğini ne belirler? Chunking, format, başlık hijyeni.</image:caption>
      <image:title>Knowledge Files: Etkili RAG için Doküman Hazırlama</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/knowledge-files-rag</loc>
    <lastmod>2026-05-11T13:49:07.283Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/knowledge-files-rag"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/knowledge-files-rag"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/knowledge-files-rag"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Yüklediğin dokümanın &apos;akıllı&apos; bulunabilirliğini ne belirler? Chunking, format, başlık hijyeni.</image:caption>
      <image:title>Knowledge Files: Etkili RAG için Doküman Hazırlama</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/custom-gpt-actions</loc>
    <lastmod>2026-05-11T13:49:07.455Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/custom-gpt-actions"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/custom-gpt-actions"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/custom-gpt-actions"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Custom GPT&apos;nin dış API çağırması: OpenAPI 3.1 spec yazma, parametre, response handling.</image:caption>
      <image:title>Actions: OpenAPI Spec ile API Entegrasyonu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/custom-gpt-actions</loc>
    <lastmod>2026-05-11T13:49:07.455Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/custom-gpt-actions"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/custom-gpt-actions"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/custom-gpt-actions"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Custom GPT&apos;nin dış API çağırması: OpenAPI 3.1 spec yazma, parametre, response handling.</image:caption>
      <image:title>Actions: OpenAPI Spec ile API Entegrasyonu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/custom-gpt-auth</loc>
    <lastmod>2026-05-11T13:49:07.642Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/custom-gpt-auth"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/custom-gpt-auth"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/custom-gpt-auth"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Actions için kimlik doğrulama: ne zaman API Key, ne zaman OAuth? Güvenlik notları.</image:caption>
      <image:title>Authentication: API Key, OAuth Senaryoları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/custom-gpt-auth</loc>
    <lastmod>2026-05-11T13:49:07.642Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/custom-gpt-auth"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/custom-gpt-auth"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/custom-gpt-auth"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Actions için kimlik doğrulama: ne zaman API Key, ne zaman OAuth? Güvenlik notları.</image:caption>
      <image:title>Authentication: API Key, OAuth Senaryoları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gpt-store-yayinlama</loc>
    <lastmod>2026-05-11T13:49:07.853Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gpt-store-yayinlama"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/gpt-store-yayinlama"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gpt-store-yayinlama"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GPT&apos;ni Store&apos;a koymak, listeleme, SEO, kullanıcı kazanma ve revenue share.</image:caption>
      <image:title>GPT Store&apos;da Yayınlama, Kategorizasyon, Para Kazanma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/gpt-store-yayinlama</loc>
    <lastmod>2026-05-11T13:49:07.853Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gpt-store-yayinlama"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/gpt-store-yayinlama"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gpt-store-yayinlama"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GPT&apos;ni Store&apos;a koymak, listeleme, SEO, kullanıcı kazanma ve revenue share.</image:caption>
      <image:title>GPT Store&apos;da Yayınlama, Kategorizasyon, Para Kazanma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/pratik-vaka-seo-gpt</loc>
    <lastmod>2026-05-11T13:49:08.069Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/pratik-vaka-seo-gpt"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/pratik-vaka-seo-gpt"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/pratik-vaka-seo-gpt"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Adım adım, dakika dakika: tek bir GPT&apos;yi kavramsallaştırmadan yayına. Kapsamlı vaka çalışması.</image:caption>
      <image:title>Pratik Vaka: &apos;Kişisel SEO Asistanı&apos; GPT&apos;sini Sıfırdan İnşa</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/pratik-vaka-seo-gpt</loc>
    <lastmod>2026-05-11T13:49:08.069Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/pratik-vaka-seo-gpt"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/pratik-vaka-seo-gpt"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/pratik-vaka-seo-gpt"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Adım adım, dakika dakika: tek bir GPT&apos;yi kavramsallaştırmadan yayına. Kapsamlı vaka çalışması.</image:caption>
      <image:title>Pratik Vaka: &apos;Kişisel SEO Asistanı&apos; GPT&apos;sini Sıfırdan İnşa</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/web-search-tool</loc>
    <lastmod>2026-05-11T13:49:08.272Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/web-search-tool"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/web-search-tool"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/web-search-tool"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Eğitim cutoff&apos;u sonrası bilgi, gerçek zamanlı veri, kaynak doğrulama — Web Search ile.</image:caption>
      <image:title>Web Search (Browse): Güncel Bilgi Çekme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/web-search-tool</loc>
    <lastmod>2026-05-11T13:49:08.272Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/web-search-tool"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/web-search-tool"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/web-search-tool"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Eğitim cutoff&apos;u sonrası bilgi, gerçek zamanlı veri, kaynak doğrulama — Web Search ile.</image:caption>
      <image:title>Web Search (Browse): Güncel Bilgi Çekme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-interpreter-detay</loc>
    <lastmod>2026-05-11T13:49:08.520Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-interpreter-detay"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/code-interpreter-detay"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-interpreter-detay"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1532153975070-2e9ab71f1b14?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 6&apos;da tanıştığımız Code Interpreter&apos;ın gelişmiş kullanımları: dosya üretimi, multi-step pipeline, performans.</image:caption>
      <image:title>Code Interpreter Sandbox Detayları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/code-interpreter-detay</loc>
    <lastmod>2026-05-11T13:49:08.520Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-interpreter-detay"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/code-interpreter-detay"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/code-interpreter-detay"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1532153975070-2e9ab71f1b14?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 6&apos;da tanıştığımız Code Interpreter&apos;ın gelişmiş kullanımları: dosya üretimi, multi-step pipeline, performans.</image:caption>
      <image:title>Code Interpreter Sandbox Detayları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/dosya-yukleme</loc>
    <lastmod>2026-05-11T13:49:08.721Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/dosya-yukleme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/dosya-yukleme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/dosya-yukleme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Hangi format ne zaman? Optimal hazırlama, multiple files, parsing tuzakları.</image:caption>
      <image:title>File Upload: PDF, DOCX, XLSX, CSV, Görsel</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/dosya-yukleme</loc>
    <lastmod>2026-05-11T13:49:08.721Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/dosya-yukleme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/dosya-yukleme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/dosya-yukleme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Hangi format ne zaman? Optimal hazırlama, multiple files, parsing tuzakları.</image:caption>
      <image:title>File Upload: PDF, DOCX, XLSX, CSV, Görsel</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/canvas</loc>
    <lastmod>2026-05-11T13:49:08.879Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/canvas"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/canvas"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/canvas"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Uzun metin/kod üzerinde ChatGPT ile satır-satır işbirliği. Edit mode, suggest, version history.</image:caption>
      <image:title>Canvas: Yeni İşbirliği Editörü</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/canvas</loc>
    <lastmod>2026-05-11T13:49:08.879Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/canvas"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/canvas"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/canvas"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Uzun metin/kod üzerinde ChatGPT ile satır-satır işbirliği. Edit mode, suggest, version history.</image:caption>
      <image:title>Canvas: Yeni İşbirliği Editörü</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/memory-yonetimi</loc>
    <lastmod>2026-05-11T13:49:09.119Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/memory-yonetimi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/memory-yonetimi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/memory-yonetimi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;nin sohbetler arası hatırladığı bilgi. Ne hatırlar, ne unutur, nasıl yönetilir?</image:caption>
      <image:title>Memory: Kalıcı Bellek Yönetimi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/memory-yonetimi</loc>
    <lastmod>2026-05-11T13:49:09.119Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/memory-yonetimi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/memory-yonetimi"/>
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      <image:caption>ChatGPT&apos;nin sohbetler arası hatırladığı bilgi. Ne hatırlar, ne unutur, nasıl yönetilir?</image:caption>
      <image:title>Memory: Kalıcı Bellek Yönetimi</image:title>
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      <image:caption>OpenAI Platform hesabı, billing setup, rate limit&apos;ler ve maliyet kontrolü.</image:caption>
      <image:title>API&apos;ye Giriş: Anahtar Alma, Faturalama, Limitler</image:title>
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      <image:caption>OpenAI Platform hesabı, billing setup, rate limit&apos;ler ve maliyet kontrolü.</image:caption>
      <image:title>API&apos;ye Giriş: Anahtar Alma, Faturalama, Limitler</image:title>
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      <image:caption>Aynı çağrıyı 3 dilde — terminal, Python, Node.js. Hangisi ne için?</image:caption>
      <image:title>İlk API Çağrısı: curl, Python, Node.js</image:title>
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      <image:caption>Aynı çağrıyı 3 dilde — terminal, Python, Node.js. Hangisi ne için?</image:caption>
      <image:title>İlk API Çağrısı: curl, Python, Node.js</image:title>
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      <image:caption>ChatGPT yanıtı tek seferde değil, akarak gelir — kullanıcı bekleme süresi azalır. SSE ile implementasyon.</image:caption>
      <image:title>Streaming Responses: SSE Pattern</image:title>
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      <image:caption>ChatGPT yanıtı tek seferde değil, akarak gelir — kullanıcı bekleme süresi azalır. SSE ile implementasyon.</image:caption>
      <image:title>Streaming Responses: SSE Pattern</image:title>
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      <image:caption>Modele &apos;şu fonksiyonları çağırabilirsin&apos; deyip, hangisini çağıracağını model seçtirme. Agent&apos;ların temeli.</image:caption>
      <image:title>Function Calling: Yapılandırılmış Tool Kullanımı</image:title>
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      <image:caption>Modele &apos;şu fonksiyonları çağırabilirsin&apos; deyip, hangisini çağıracağını model seçtirme. Agent&apos;ların temeli.</image:caption>
      <image:title>Function Calling: Yapılandırılmış Tool Kullanımı</image:title>
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      <image:caption>Modül 4&apos;te giriş yaptık; burada API tarafında detayları, Zod entegrasyonu, performans.</image:caption>
      <image:title>Structured Outputs: JSON Schema ile Garantili Format</image:title>
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      <image:caption>Modül 4&apos;te giriş yaptık; burada API tarafında detayları, Zod entegrasyonu, performans.</image:caption>
      <image:title>Structured Outputs: JSON Schema ile Garantili Format</image:title>
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    <priority>0.70</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/assistants-api"/>
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      <image:caption>Custom GPT&apos;nin programatik karşılığı: stateful asistanlar, kalıcı thread&apos;ler, tool ekosistemi.</image:caption>
      <image:title>Assistants API: Threads, Runs, Tools</image:title>
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    <priority>0.60</priority>
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      <image:caption>Custom GPT&apos;nin programatik karşılığı: stateful asistanlar, kalıcı thread&apos;ler, tool ekosistemi.</image:caption>
      <image:title>Assistants API: Threads, Runs, Tools</image:title>
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    <priority>0.70</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/embeddings-rag"/>
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      <image:caption>Metni vektöre dönüştürme, semantic search, RAG mimarisi. Custom GPT Knowledge altında bunlar var.</image:caption>
      <image:title>Embeddings ve Vector Search: RAG Temelleri</image:title>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/embeddings-rag"/>
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      <image:caption>Metni vektöre dönüştürme, semantic search, RAG mimarisi. Custom GPT Knowledge altında bunlar var.</image:caption>
      <image:title>Embeddings ve Vector Search: RAG Temelleri</image:title>
    </image:image>
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    <priority>0.70</priority>
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      <image:caption>10K kullanıcılı uygulamada API bütçesi nasıl yönetilir? Caching, model seçimi, prompt optimizasyon.</image:caption>
      <image:title>Token Ekonomisi: Maliyet Optimizasyonu</image:title>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/token-ekonomi"/>
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      <image:caption>10K kullanıcılı uygulamada API bütçesi nasıl yönetilir? Caching, model seçimi, prompt optimizasyon.</image:caption>
      <image:title>Token Ekonomisi: Maliyet Optimizasyonu</image:title>
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    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/gunluk-rutin-entegrasyon"/>
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      <image:caption>Sabahtan akşama 12 noktada ChatGPT — her birinin promptu hazır.</image:caption>
      <image:title>ChatGPT&apos;yi Günlük Rutine Entegre Etmek</image:title>
    </image:image>
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    <priority>0.60</priority>
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      <image:caption>Sabahtan akşama 12 noktada ChatGPT — her birinin promptu hazır.</image:caption>
      <image:title>ChatGPT&apos;yi Günlük Rutine Entegre Etmek</image:title>
    </image:image>
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    <priority>0.70</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/toplanti-notlari"/>
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      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Otomatik transkript + ChatGPT ile özet ve aksiyon çıkarma. Toplantı verimliliği 3x.</image:caption>
      <image:title>Toplantı Notları, Özet ve Aksiyon Çıkarımı</image:title>
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  <url>
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    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/toplanti-notlari"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/toplanti-notlari"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/toplanti-notlari"/>
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      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Otomatik transkript + ChatGPT ile özet ve aksiyon çıkarma. Toplantı verimliliği 3x.</image:caption>
      <image:title>Toplantı Notları, Özet ve Aksiyon Çıkarımı</image:title>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/karar-verme-cerceveler</loc>
    <lastmod>2026-05-11T13:49:11.230Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/karar-verme-cerceveler"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/karar-verme-cerceveler"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/karar-verme-cerceveler"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Klasik karar çerçeveleri + ChatGPT promptları. Kişisel ve iş kararları için.</image:caption>
      <image:title>Karar Verme Çerçeveleri: Eisenhower, OODA, RICE</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/karar-verme-cerceveler</loc>
    <lastmod>2026-05-11T13:49:11.230Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/karar-verme-cerceveler"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/karar-verme-cerceveler"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/karar-verme-cerceveler"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Klasik karar çerçeveleri + ChatGPT promptları. Kişisel ve iş kararları için.</image:caption>
      <image:title>Karar Verme Çerçeveleri: Eisenhower, OODA, RICE</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ogrenme-plani</loc>
    <lastmod>2026-05-11T13:49:11.429Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ogrenme-plani"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/ogrenme-plani"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ogrenme-plani"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1546900703-cf06143d1239?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;den özelleştirilmiş 7 günlük öğrenme planı isteme. Konu seçim → plan → günlük takip.</image:caption>
      <image:title>Yeni Bir Konuyu 7 Günde Öğrenme Planı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/ogrenme-plani</loc>
    <lastmod>2026-05-11T13:49:11.429Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ogrenme-plani"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/ogrenme-plani"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/ogrenme-plani"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1546900703-cf06143d1239?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;den özelleştirilmiş 7 günlük öğrenme planı isteme. Konu seçim → plan → günlük takip.</image:caption>
      <image:title>Yeni Bir Konuyu 7 Günde Öğrenme Planı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/otomasyonlar</loc>
    <lastmod>2026-05-11T13:49:11.569Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/otomasyonlar"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/otomasyonlar"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/otomasyonlar"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1531746790731-6c087fecd65a?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT + Zapier/Make ile rutin işlerin otomatikleştirilmesi. 5 hazır şablon.</image:caption>
      <image:title>E-posta ve Slack/Discord Otomasyonu (Zapier, Make)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/otomasyonlar</loc>
    <lastmod>2026-05-11T13:49:11.569Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/otomasyonlar"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/otomasyonlar"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/otomasyonlar"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1531746790731-6c087fecd65a?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT + Zapier/Make ile rutin işlerin otomatikleştirilmesi. 5 hazır şablon.</image:caption>
      <image:title>E-posta ve Slack/Discord Otomasyonu (Zapier, Make)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/second-brain</loc>
    <lastmod>2026-05-11T13:49:11.787Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/second-brain"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/second-brain"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/second-brain"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Notlar, fikirler, belgeler — ChatGPT ile arama yapılabilir kişisel bilgi tabanı.</image:caption>
      <image:title>Kişisel &apos;Second Brain&apos; Setup&apos;ı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/second-brain</loc>
    <lastmod>2026-05-11T13:49:11.787Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/second-brain"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/second-brain"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/second-brain"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Notlar, fikirler, belgeler — ChatGPT ile arama yapılabilir kişisel bilgi tabanı.</image:caption>
      <image:title>Kişisel &apos;Second Brain&apos; Setup&apos;ı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-pazarlama</loc>
    <lastmod>2026-05-11T13:49:11.988Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-pazarlama"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-pazarlama"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-pazarlama"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>10 hazır pazarlama use case + her birinin promptu. Strateji, ad copy, SEO, analiz.</image:caption>
      <image:title>Pazarlama, Reklam ve SEO</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-pazarlama</loc>
    <lastmod>2026-05-11T13:49:11.988Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-pazarlama"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-pazarlama"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-pazarlama"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>10 hazır pazarlama use case + her birinin promptu. Strateji, ad copy, SEO, analiz.</image:caption>
      <image:title>Pazarlama, Reklam ve SEO</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-egitim</loc>
    <lastmod>2026-05-11T13:49:12.157Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-egitim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-egitim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-egitim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1546900703-cf06143d1239?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Öğretmenler ve eğitmenler için ChatGPT — ders planı, sınav, ödev geri bildirim, materyal üretimi.</image:caption>
      <image:title>Eğitim ve Öğretmenlik</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-egitim</loc>
    <lastmod>2026-05-11T13:49:12.157Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-egitim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-egitim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-egitim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1546900703-cf06143d1239?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Öğretmenler ve eğitmenler için ChatGPT — ders planı, sınav, ödev geri bildirim, materyal üretimi.</image:caption>
      <image:title>Eğitim ve Öğretmenlik</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-saglik</loc>
    <lastmod>2026-05-11T13:49:12.318Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-saglik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-saglik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-saglik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sağlık alanında ChatGPT&apos;nin etik kullanımı: bilgilendirme, beslenme, fitness, mental sağlık.</image:caption>
      <image:title>Sağlık (Klinik Olmayan Bilgilendirme)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-saglik</loc>
    <lastmod>2026-05-11T13:49:12.318Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-saglik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-saglik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-saglik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sağlık alanında ChatGPT&apos;nin etik kullanımı: bilgilendirme, beslenme, fitness, mental sağlık.</image:caption>
      <image:title>Sağlık (Klinik Olmayan Bilgilendirme)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-hukuk</loc>
    <lastmod>2026-05-11T13:49:12.515Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-hukuk"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-hukuk"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-hukuk"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Hukuki metinleri anlama, sözleşme taslağı, hukuki sorgulara bilgilendirici yanıt.</image:caption>
      <image:title>Hukuk (Sözleşme Taslağı, Bilgilendirme)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-hukuk</loc>
    <lastmod>2026-05-11T13:49:12.515Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-hukuk"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-hukuk"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-hukuk"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Hukuki metinleri anlama, sözleşme taslağı, hukuki sorgulara bilgilendirici yanıt.</image:caption>
      <image:title>Hukuk (Sözleşme Taslağı, Bilgilendirme)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-finans</loc>
    <lastmod>2026-05-11T13:49:12.690Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-finans"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-finans"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-finans"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Finansal modelleme, oran analizi, raporlama, bütçe yönetimi — ChatGPT ile.</image:caption>
      <image:title>Finans ve Muhasebe Asistanlığı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-finans</loc>
    <lastmod>2026-05-11T13:49:12.690Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-finans"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-finans"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-finans"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Finansal modelleme, oran analizi, raporlama, bütçe yönetimi — ChatGPT ile.</image:caption>
      <image:title>Finans ve Muhasebe Asistanlığı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-yazilim</loc>
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    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-yazilim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-yazilim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-yazilim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Daily standup notu, code review, mimari karar, deployment troubleshooting.</image:caption>
      <image:title>Yazılım Geliştirme Pratikleri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-yazilim</loc>
    <lastmod>2026-05-11T13:49:12.844Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-yazilim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-yazilim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-yazilim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Daily standup notu, code review, mimari karar, deployment troubleshooting.</image:caption>
      <image:title>Yazılım Geliştirme Pratikleri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-ik</loc>
    <lastmod>2026-05-11T13:49:13.003Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-ik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-ik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-ik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>İş ilanı yazma, mülakat soruları, CV inceleme, çalışan geri bildirim, performans değerlendirme.</image:caption>
      <image:title>İnsan Kaynakları ve Mülakatlar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-ik</loc>
    <lastmod>2026-05-11T13:49:13.003Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-ik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/sektor-ik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/sektor-ik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>İş ilanı yazma, mülakat soruları, CV inceleme, çalışan geri bildirim, performans değerlendirme.</image:caption>
      <image:title>İnsan Kaynakları ve Mülakatlar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/halusinasyon</loc>
    <lastmod>2026-05-11T13:49:13.153Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/halusinasyon"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/halusinasyon"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/halusinasyon"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;nin &apos;gerçekmiş gibi&apos; yanlış bilgi üretmesi. Niçin olur, nasıl tanırsın, nasıl kaçınırsın?</image:caption>
      <image:title>Halüsinasyon: Tanımı, Tanıma, Kaçınma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/halusinasyon</loc>
    <lastmod>2026-05-11T13:49:13.153Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/halusinasyon"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/halusinasyon"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/halusinasyon"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;nin &apos;gerçekmiş gibi&apos; yanlış bilgi üretmesi. Niçin olur, nasıl tanırsın, nasıl kaçınırsın?</image:caption>
      <image:title>Halüsinasyon: Tanımı, Tanıma, Kaçınma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/bias-onyargilar</loc>
    <lastmod>2026-05-11T13:49:13.328Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/bias-onyargilar"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/bias-onyargilar"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/bias-onyargilar"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM&apos;ler eğitim verisindeki ön yargıları **öğrenir ve tekrarlar**. Tanıma ve azaltma stratejileri.</image:caption>
      <image:title>Bias ve Önyargılar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/bias-onyargilar</loc>
    <lastmod>2026-05-11T13:49:13.328Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/bias-onyargilar"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/bias-onyargilar"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/bias-onyargilar"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM&apos;ler eğitim verisindeki ön yargıları **öğrenir ve tekrarlar**. Tanıma ve azaltma stratejileri.</image:caption>
      <image:title>Bias ve Önyargılar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/veri-gizliligi</loc>
    <lastmod>2026-05-11T13:49:13.477Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/veri-gizliligi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/veri-gizliligi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/veri-gizliligi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;ye ne gönderebilir, ne göndermemelisin? KVKK, GDPR, kurumsal politika.</image:caption>
      <image:title>Veri Gizliliği: Nelere Dikkat?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/veri-gizliligi</loc>
    <lastmod>2026-05-11T13:49:13.477Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/veri-gizliligi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/veri-gizliligi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/veri-gizliligi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;ye ne gönderebilir, ne göndermemelisin? KVKK, GDPR, kurumsal politika.</image:caption>
      <image:title>Veri Gizliliği: Nelere Dikkat?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/telif-atif</loc>
    <lastmod>2026-05-11T13:49:13.643Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/telif-atif"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/telif-atif"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/telif-atif"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;in ürettiği içeriğin sahibi kim? Eğitim verisindeki telif sorunları, atıf gerekliliği.</image:caption>
      <image:title>Telif Hakkı ve Atıf</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/telif-atif</loc>
    <lastmod>2026-05-11T13:49:13.643Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/telif-atif"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/telif-atif"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/telif-atif"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;in ürettiği içeriğin sahibi kim? Eğitim verisindeki telif sorunları, atıf gerekliliği.</image:caption>
      <image:title>Telif Hakkı ve Atıf</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/akademik-durustluk</loc>
    <lastmod>2026-05-11T13:49:13.814Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/akademik-durustluk"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/akademik-durustluk"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/akademik-durustluk"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;nin akademik kullanımı: nerede etik, nerede intihal? Tespit araçları ve çözümler.</image:caption>
      <image:title>Akademik Dürüstlük ve Plagiarism Detection</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/akademik-durustluk</loc>
    <lastmod>2026-05-11T13:49:13.814Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/akademik-durustluk"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/akademik-durustluk"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/akademik-durustluk"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ChatGPT&apos;nin akademik kullanımı: nerede etik, nerede intihal? Tespit araçları ve çözümler.</image:caption>
      <image:title>Akademik Dürüstlük ve Plagiarism Detection</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/kvkk-ai-act</loc>
    <lastmod>2026-05-11T13:49:13.961Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/kvkk-ai-act"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/kvkk-ai-act"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/kvkk-ai-act"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>AI&apos;ya yönelik 2025-2026 yasal çerçeve: Türkiye KVKK güncellemeleri, AB AI Act, ABD eyalet yasaları.</image:caption>
      <image:title>Türkiye KVKK + AB AI Act Çerçevesi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/kvkk-ai-act</loc>
    <lastmod>2026-05-11T13:49:13.961Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/kvkk-ai-act"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/kvkk-ai-act"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/kvkk-ai-act"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>AI&apos;ya yönelik 2025-2026 yasal çerçeve: Türkiye KVKK güncellemeleri, AB AI Act, ABD eyalet yasaları.</image:caption>
      <image:title>Türkiye KVKK + AB AI Act Çerçevesi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-saglikli-yasam</loc>
    <lastmod>2026-05-11T13:49:14.111Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-saglikli-yasam"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/vaka-saglikli-yasam"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-saglikli-yasam"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sıfırdan sağlıklı yaşam asistanı GPT&apos;si: kişiselleştirme, plan üretimi, takip, motivasyon.</image:caption>
      <image:title>Vaka 1: 30 Günlük Sağlıklı Yaşam Asistanı (Custom GPT)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/vaka-saglikli-yasam</loc>
    <lastmod>2026-05-11T13:49:14.111Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-saglikli-yasam"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/vaka-saglikli-yasam"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-saglikli-yasam"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sıfırdan sağlıklı yaşam asistanı GPT&apos;si: kişiselleştirme, plan üretimi, takip, motivasyon.</image:caption>
      <image:title>Vaka 1: 30 Günlük Sağlıklı Yaşam Asistanı (Custom GPT)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-saas-lansman</loc>
    <lastmod>2026-05-11T13:49:14.268Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-saas-lansman"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/vaka-saas-lansman"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-saas-lansman"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Yeni ürün lansmanı için 12 haftalık pazarlama pipeline&apos;ı. ChatGPT&apos;nin her aşamadaki rolü.</image:caption>
      <image:title>Vaka 2: SaaS Ürün Lansmanı — Sıfırdan Pazarlama Pipeline&apos;ı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/vaka-saas-lansman</loc>
    <lastmod>2026-05-11T13:49:14.268Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-saas-lansman"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/vaka-saas-lansman"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-saas-lansman"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Yeni ürün lansmanı için 12 haftalık pazarlama pipeline&apos;ı. ChatGPT&apos;nin her aşamadaki rolü.</image:caption>
      <image:title>Vaka 2: SaaS Ürün Lansmanı — Sıfırdan Pazarlama Pipeline&apos;ı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-kitap-yazma</loc>
    <lastmod>2026-05-11T13:49:14.473Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-kitap-yazma"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/vaka-kitap-yazma"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-kitap-yazma"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1497032628192-86f99bcd76bc?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>200 sayfalık bir kitabı 6 ayda yazma süreci. ChatGPT&apos;nin yardımcı olduğu 7 aşama.</image:caption>
      <image:title>Vaka 3: Kitap Yazma Workflow&apos;u — Outline&apos;dan Yayına</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/vaka-kitap-yazma</loc>
    <lastmod>2026-05-11T13:49:14.473Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-kitap-yazma"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/vaka-kitap-yazma"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-kitap-yazma"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1497032628192-86f99bcd76bc?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>200 sayfalık bir kitabı 6 ayda yazma süreci. ChatGPT&apos;nin yardımcı olduğu 7 aşama.</image:caption>
      <image:title>Vaka 3: Kitap Yazma Workflow&apos;u — Outline&apos;dan Yayına</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-kurs-uretim</loc>
    <lastmod>2026-05-11T13:49:14.654Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-kurs-uretim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/vaka-kurs-uretim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-kurs-uretim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sıfırdan yayına 8 saatlik bir online kurs nasıl üretilir? Müfredat, video script, alıştırma, sınav.</image:caption>
      <image:title>Vaka 4: Online Kurs Üretim Pipeline&apos;ı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/vaka-kurs-uretim</loc>
    <lastmod>2026-05-11T13:49:14.654Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-kurs-uretim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/vaka-kurs-uretim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-kurs-uretim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sıfırdan yayına 8 saatlik bir online kurs nasıl üretilir? Müfredat, video script, alıştırma, sınav.</image:caption>
      <image:title>Vaka 4: Online Kurs Üretim Pipeline&apos;ı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-yatirim-arastirma</loc>
    <lastmod>2026-05-11T13:49:14.836Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-yatirim-arastirma"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/vaka-yatirim-arastirma"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-yatirim-arastirma"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Hisse, sektör, makro analiz için ChatGPT&apos;yi araştırma asistanı yapma — yatırım tavsiyesi değil.</image:caption>
      <image:title>Vaka 5: Yatırım Araştırma Asistanı (Etik Sınırlarla)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/vaka-yatirim-arastirma</loc>
    <lastmod>2026-05-11T13:49:14.836Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-yatirim-arastirma"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/vaka-yatirim-arastirma"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/vaka-yatirim-arastirma"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Hisse, sektör, makro analiz için ChatGPT&apos;yi araştırma asistanı yapma — yatırım tavsiyesi değil.</image:caption>
      <image:title>Vaka 5: Yatırım Araştırma Asistanı (Etik Sınırlarla)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-bilgi-tabani</loc>
    <lastmod>2026-05-11T13:49:14.987Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-bilgi-tabani"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/proje-bilgi-tabani"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-bilgi-tabani"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tüm notlarını sorgulanabilir hale getir. Custom GPT + Notion/Obsidian export pipeline.</image:caption>
      <image:title>Proje 1: Kişisel Bilgi Tabanı (Custom GPT + Knowledge)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/proje-bilgi-tabani</loc>
    <lastmod>2026-05-11T13:49:14.987Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-bilgi-tabani"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/proje-bilgi-tabani"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-bilgi-tabani"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tüm notlarını sorgulanabilir hale getir. Custom GPT + Notion/Obsidian export pipeline.</image:caption>
      <image:title>Proje 1: Kişisel Bilgi Tabanı (Custom GPT + Knowledge)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-blog-pipeline</loc>
    <lastmod>2026-05-11T13:49:15.163Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-blog-pipeline"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/proje-blog-pipeline"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-blog-pipeline"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir Notion sayfasından otomatik blog yazısı + meta + sosyal post + yayın akışı.</image:caption>
      <image:title>Proje 2: Otomatik Blog Yayın Hattı (API + Webhook)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/proje-blog-pipeline</loc>
    <lastmod>2026-05-11T13:49:15.163Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-blog-pipeline"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/proje-blog-pipeline"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-blog-pipeline"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir Notion sayfasından otomatik blog yazısı + meta + sosyal post + yayın akışı.</image:caption>
      <image:title>Proje 2: Otomatik Blog Yayın Hattı (API + Webhook)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-destek-bot</loc>
    <lastmod>2026-05-11T13:49:15.308Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-destek-bot"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/proje-destek-bot"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-destek-bot"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Knowledge base + ticket oluşturma + escalation rules — uçtan uca destek botu.</image:caption>
      <image:title>Proje 3: Müşteri Destek Botu (Custom GPT + Actions)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/proje-destek-bot</loc>
    <lastmod>2026-05-11T13:49:15.308Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-destek-bot"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/proje-destek-bot"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-destek-bot"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Knowledge base + ticket oluşturma + escalation rules — uçtan uca destek botu.</image:caption>
      <image:title>Proje 3: Müşteri Destek Botu (Custom GPT + Actions)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-dashboard</loc>
    <lastmod>2026-05-11T13:49:15.479Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-dashboard"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/proje-dashboard"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-dashboard"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Aylık otomatik dashboard üretimi: CSV yükle, ChatGPT analiz + grafikler + insights yazsın.</image:caption>
      <image:title>Proje 4: Veri Analizi Dashboard&apos;u (Code Interpreter)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/proje-dashboard</loc>
    <lastmod>2026-05-11T13:49:15.479Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-dashboard"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/proje-dashboard"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-dashboard"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Aylık otomatik dashboard üretimi: CSV yükle, ChatGPT analiz + grafikler + insights yazsın.</image:caption>
      <image:title>Proje 4: Veri Analizi Dashboard&apos;u (Code Interpreter)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-lokalizasyon</loc>
    <lastmod>2026-05-11T13:49:15.682Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-lokalizasyon"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/proje-lokalizasyon"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-lokalizasyon"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>İçeriklerini 10 dile otomatik çevirme + kalite kontrol + re-import pipeline&apos;ı.</image:caption>
      <image:title>Proje 5: Çok Dilli Lokalizasyon Sistemi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/proje-lokalizasyon</loc>
    <lastmod>2026-05-11T13:49:15.682Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-lokalizasyon"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/chatgpt-ustaligi/proje-lokalizasyon"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/chatgpt-ustaligi/proje-lokalizasyon"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>İçeriklerini 10 dile otomatik çevirme + kalite kontrol + re-import pipeline&apos;ı.</image:caption>
      <image:title>Proje 5: Çok Dilli Lokalizasyon Sistemi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/claude-nedir</loc>
    <lastmod>2026-05-13T18:49:29.356Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/claude-nedir"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/claude-nedir"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/claude-nedir"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude&apos;un ne olduğunu, geleneksel chatbot&apos;lardan ne ile ayrıldığını ve günlük çalışmanı nasıl değiştireceğini sıfırdan öğren.</image:caption>
      <image:title>Claude Nedir? Yapay Zekâ Asistanlarının Yeni Nesli</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/claude-nedir</loc>
    <lastmod>2026-05-13T18:49:29.356Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/claude-nedir"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/claude-nedir"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/claude-nedir"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude&apos;un ne olduğunu, geleneksel chatbot&apos;lardan ne ile ayrıldığını ve günlük çalışmanı nasıl değiştireceğini sıfırdan öğren.</image:caption>
      <image:title>Claude Nedir? Yapay Zekâ Asistanlarının Yeni Nesli</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/anthropic-constitutional-ai</loc>
    <lastmod>2026-05-13T08:35:24.470Z</lastmod>
    <changefreq>monthly</changefreq>
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      <image:title>Açıklık ve Spesifiklik: Belirsiz Prompt&apos;tan Cerrahi Prompt&apos;a</image:title>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/xml-yapilandirilmis-prompt"/>
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      <image:caption>Anthropic&apos;in dokümantasyonunda öne çıkan XML etiketleri tekniği ile prompt&apos;larını parse&apos;lanabilir, sürdürülebilir ve test edilebilir hale getir.</image:caption>
      <image:title>XML Etiketleri ile Yapılandırılmış Prompt&apos;lar</image:title>
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      <image:caption>Üretim seviyesi prompt&apos;lar yazılım gibi yönetilmelidir: şablon, parametre, versiyon, test, monitoring. Bu derste prompt&apos;ları kod gibi disiplinli yönetmeyi öğreneceğiz.</image:caption>
      <image:title>Prompt Şablonu Tasarlama ve Versiyonlama</image:title>
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    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/prompt-sablon-tasarimi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/prompt-sablon-tasarimi"/>
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      <image:caption>Üretim seviyesi prompt&apos;lar yazılım gibi yönetilmelidir: şablon, parametre, versiyon, test, monitoring. Bu derste prompt&apos;ları kod gibi disiplinli yönetmeyi öğreneceğiz.</image:caption>
      <image:title>Prompt Şablonu Tasarlama ve Versiyonlama</image:title>
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      <image:caption>Claude&apos;un kişiliğini, sınırlarını ve davranış çerçevesini sistem prompt&apos;uyla nasıl tasarlarsın? Üretime hazır persona kalıpları bu derste.</image:caption>
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    <changefreq>monthly</changefreq>
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      <image:caption>Claude&apos;un kişiliğini, sınırlarını ve davranış çerçevesini sistem prompt&apos;uyla nasıl tasarlarsın? Üretime hazır persona kalıpları bu derste.</image:caption>
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    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/output-format-kontrolu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/output-format-kontrolu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/output-format-kontrolu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551836022-deb4988cc6c0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude&apos;un çıktısını parse edilebilir, tutarlı ve hatasız almak için format kontrolünün üç tekniğini ustalaş: schema, prefill, validator-loop.</image:caption>
      <image:title>Output Format Kontrolü: JSON, Markdown, Tablo</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/output-format-kontrolu</loc>
    <lastmod>2026-05-13T09:36:16.318Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/output-format-kontrolu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/output-format-kontrolu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/output-format-kontrolu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551836022-deb4988cc6c0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude&apos;un çıktısını parse edilebilir, tutarlı ve hatasız almak için format kontrolünün üç tekniğini ustalaş: schema, prefill, validator-loop.</image:caption>
      <image:title>Output Format Kontrolü: JSON, Markdown, Tablo</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/task-decomposition</loc>
    <lastmod>2026-05-13T09:37:27.734Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/task-decomposition"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/task-decomposition"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/task-decomposition"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Karmaşık görevleri tek prompt&apos;la çözmeye çalışmak yerine, modüler alt görevlere bölerek çöz. Daha doğru, daha test edilebilir, daha ucuz.</image:caption>
      <image:title>Çoklu Adım Görev Ayrıştırma (Task Decomposition)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/task-decomposition</loc>
    <lastmod>2026-05-13T09:37:27.734Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/task-decomposition"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/task-decomposition"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/task-decomposition"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Karmaşık görevleri tek prompt&apos;la çözmeye çalışmak yerine, modüler alt görevlere bölerek çöz. Daha doğru, daha test edilebilir, daha ucuz.</image:caption>
      <image:title>Çoklu Adım Görev Ayrıştırma (Task Decomposition)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/prompt-debugging</loc>
    <lastmod>2026-05-13T11:36:39.844Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/prompt-debugging"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/prompt-debugging"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/prompt-debugging"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Prompt çalışmıyor mu? Bu derste hatayı sistematik bulmak için çalışan bir teşhis ağacı, log stratejisi ve model değişikliği checklist&apos;i öğreneceksin.</image:caption>
      <image:title>Prompt Hata Ayıklama: Neden Çalışmıyor?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/prompt-debugging</loc>
    <lastmod>2026-05-13T11:36:39.844Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/prompt-debugging"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/prompt-debugging"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/prompt-debugging"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Prompt çalışmıyor mu? Bu derste hatayı sistematik bulmak için çalışan bir teşhis ağacı, log stratejisi ve model değişikliği checklist&apos;i öğreneceksin.</image:caption>
      <image:title>Prompt Hata Ayıklama: Neden Çalışmıyor?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/token-ekonomisi</loc>
    <lastmod>2026-05-13T11:36:56.690Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/token-ekonomisi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/token-ekonomisi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/token-ekonomisi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Aynı kaliteyi %50-90 daha az maliyetle üretmek için token ekonomisi: prompt caching, model katmanlama, output kısıtlama, batch.</image:caption>
      <image:title>Token Ekonomisi ve Maliyet Optimizasyonu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/token-ekonomisi</loc>
    <lastmod>2026-05-13T11:36:56.690Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/token-ekonomisi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/token-ekonomisi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/token-ekonomisi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Aynı kaliteyi %50-90 daha az maliyetle üretmek için token ekonomisi: prompt caching, model katmanlama, output kısıtlama, batch.</image:caption>
      <image:title>Token Ekonomisi ve Maliyet Optimizasyonu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/kod-uretimi-sifirdan</loc>
    <lastmod>2026-05-13T11:38:00.971Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/kod-uretimi-sifirdan"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/kod-uretimi-sifirdan"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/kod-uretimi-sifirdan"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Net bir spec&apos;ten Claude&apos;la production-ready fonksiyon / sınıf üretmenin akışı: tip imzası, edge case, test, doc.</image:caption>
      <image:title>Sıfırdan Fonksiyon ve Sınıf Üretimi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/kod-uretimi-sifirdan</loc>
    <lastmod>2026-05-13T11:38:00.971Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/kod-uretimi-sifirdan"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/kod-uretimi-sifirdan"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/kod-uretimi-sifirdan"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Net bir spec&apos;ten Claude&apos;la production-ready fonksiyon / sınıf üretmenin akışı: tip imzası, edge case, test, doc.</image:caption>
      <image:title>Sıfırdan Fonksiyon ve Sınıf Üretimi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/kod-inceleme-refactor</loc>
    <lastmod>2026-05-11T14:23:26.544Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/kod-inceleme-refactor"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/kod-inceleme-refactor"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/kod-inceleme-refactor"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir code reviewer olarak Claude&apos;u nasıl kullanırsın: pattern bulma, refactor önerisi, performans tuning ve güvenlik review.</image:caption>
      <image:title>Kod İnceleme, Refactor ve Optimizasyon</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/kod-inceleme-refactor</loc>
    <lastmod>2026-05-11T14:23:26.544Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/kod-inceleme-refactor"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/kod-inceleme-refactor"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/kod-inceleme-refactor"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir code reviewer olarak Claude&apos;u nasıl kullanırsın: pattern bulma, refactor önerisi, performans tuning ve güvenlik review.</image:caption>
      <image:title>Kod İnceleme, Refactor ve Optimizasyon</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/bug-avi</loc>
    <lastmod>2026-05-11T13:48:32.792Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/bug-avi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/bug-avi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/bug-avi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1542903660-eedba2cda473?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir hatayı Claude&apos;la sistematik bulmanın akışı: minimum repro, hipotez, izolasyon, fix, regresyon testi.</image:caption>
      <image:title>Bug Avı: Stack Trace&apos;ten Çözüme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/bug-avi</loc>
    <lastmod>2026-05-11T13:48:32.792Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/bug-avi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/bug-avi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/bug-avi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1542903660-eedba2cda473?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir hatayı Claude&apos;la sistematik bulmanın akışı: minimum repro, hipotez, izolasyon, fix, regresyon testi.</image:caption>
      <image:title>Bug Avı: Stack Trace&apos;ten Çözüme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/test-yazma</loc>
    <lastmod>2026-05-11T13:48:32.890Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/test-yazma"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/test-yazma"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/test-yazma"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude&apos;la test yazmanın akışı: birim, entegrasyon, property-based, snapshot. Coverage hedefleri ve hangi testleri yazmamak.</image:caption>
      <image:title>Test Yazma, TDD ve Coverage Stratejileri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/test-yazma</loc>
    <lastmod>2026-05-11T13:48:32.890Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/test-yazma"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/test-yazma"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/test-yazma"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude&apos;la test yazmanın akışı: birim, entegrasyon, property-based, snapshot. Coverage hedefleri ve hangi testleri yazmamak.</image:caption>
      <image:title>Test Yazma, TDD ve Coverage Stratejileri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/dokuman-uretimi</loc>
    <lastmod>2026-05-11T13:49:11.431Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/dokuman-uretimi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/dokuman-uretimi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/dokuman-uretimi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude&apos;la docstring, README, mimari kararı belgesi, runbook üret. Dokümantasyon borç biriktiren değil, gelir üreten bir varlık olur.</image:caption>
      <image:title>Dokümantasyon, Docstring ve README Üretimi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/dokuman-uretimi</loc>
    <lastmod>2026-05-11T13:49:11.431Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/dokuman-uretimi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/dokuman-uretimi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/dokuman-uretimi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude&apos;la docstring, README, mimari kararı belgesi, runbook üret. Dokümantasyon borç biriktiren değil, gelir üreten bir varlık olur.</image:caption>
      <image:title>Dokümantasyon, Docstring ve README Üretimi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/claude-code-cli</loc>
    <lastmod>2026-05-11T13:49:30.583Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/claude-code-cli"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/claude-code-cli"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/claude-code-cli"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude Code CLI ile terminalde Claude — proje genel haritası, dosya düzenleme, kod çalıştırma, hooks ve plan mode dahil.</image:caption>
      <image:title>Claude Code: Terminal&apos;de Çalışma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/claude-code-cli</loc>
    <lastmod>2026-05-11T13:49:30.583Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/claude-code-cli"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/claude-code-cli"/>
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      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude Code CLI ile terminalde Claude — proje genel haritası, dosya düzenleme, kod çalıştırma, hooks ve plan mode dahil.</image:caption>
      <image:title>Claude Code: Terminal&apos;de Çalışma</image:title>
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    <priority>0.70</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/blog-uzun-form"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/blog-uzun-form"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir konu fikrinden 2.000-4.000 kelimelik kaliteli uzun form içeriğe Claude&apos;la nasıl gidersin? SEO, ton ve özgünlük dahil tam akış.</image:caption>
      <image:title>Blog, Makale ve Uzun Form İçerik</image:title>
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  <url>
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    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
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    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/blog-uzun-form"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir konu fikrinden 2.000-4.000 kelimelik kaliteli uzun form içeriğe Claude&apos;la nasıl gidersin? SEO, ton ve özgünlük dahil tam akış.</image:caption>
      <image:title>Blog, Makale ve Uzun Form İçerik</image:title>
    </image:image>
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  <url>
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    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/email-yazismalari"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/email-yazismalari"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/email-yazismalari"/>
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      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Soğuk satış e-postası, müşteri kapatma, kötü haber duyurma, brief yazma — iş yazışmasının her tipi için kanıtlanmış prompt kalıpları.</image:caption>
      <image:title>E-posta, Brief ve İş Yazışmaları</image:title>
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  <url>
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    <lastmod>2026-05-11T13:48:33.421Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/email-yazismalari"/>
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      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Soğuk satış e-postası, müşteri kapatma, kötü haber duyurma, brief yazma — iş yazışmasının her tipi için kanıtlanmış prompt kalıpları.</image:caption>
      <image:title>E-posta, Brief ve İş Yazışmaları</image:title>
    </image:image>
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  <url>
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    <lastmod>2026-05-11T13:48:33.507Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/yaraticik-yazim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/yaraticik-yazim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/yaraticik-yazim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Karakter, çatışma, mizansen, diyaloğun temel kuralları + Claude&apos;la yaratıcı yazımın etiği. Roman tasarımından kısa film senaryosuna.</image:caption>
      <image:title>Hikâye, Senaryo ve Karakter Geliştirme</image:title>
    </image:image>
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  <url>
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    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/yaraticik-yazim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/yaraticik-yazim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/yaraticik-yazim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Karakter, çatışma, mizansen, diyaloğun temel kuralları + Claude&apos;la yaratıcı yazımın etiği. Roman tasarımından kısa film senaryosuna.</image:caption>
      <image:title>Hikâye, Senaryo ve Karakter Geliştirme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/ceviri-yerellestirme</loc>
    <lastmod>2026-05-11T13:51:20.062Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/ceviri-yerellestirme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/ceviri-yerellestirme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/ceviri-yerellestirme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sadece kelime çevirisi değil — kültürel uyum, marka sesi, terminoloji listesi (TM) ve QA döngüsü. Türkçe-İngilizce odaklı.</image:caption>
      <image:title>Çeviri, Yerelleştirme ve Stil Tutarlılığı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/ceviri-yerellestirme</loc>
    <lastmod>2026-05-11T13:51:20.062Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/ceviri-yerellestirme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/ceviri-yerellestirme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/ceviri-yerellestirme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sadece kelime çevirisi değil — kültürel uyum, marka sesi, terminoloji listesi (TM) ve QA döngüsü. Türkçe-İngilizce odaklı.</image:caption>
      <image:title>Çeviri, Yerelleştirme ve Stil Tutarlılığı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/ses-stil-transferi</loc>
    <lastmod>2026-05-11T13:51:16.404Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/ses-stil-transferi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/ses-stil-transferi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/ses-stil-transferi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Yazılı sesini Claude&apos;a aşılamak — kişisel stilin, kurum sesin veya geçmiş yazıların referans alınarak tutarlı çıktı üretmek.</image:caption>
      <image:title>Sesi ve Tonu Korumak: Stil Transferi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/ses-stil-transferi</loc>
    <lastmod>2026-05-11T13:51:16.404Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/ses-stil-transferi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/ses-stil-transferi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/ses-stil-transferi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Yazılı sesini Claude&apos;a aşılamak — kişisel stilin, kurum sesin veya geçmiş yazıların referans alınarak tutarlı çıktı üretmek.</image:caption>
      <image:title>Sesi ve Tonu Korumak: Stil Transferi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/csv-json-tablo</loc>
    <lastmod>2026-05-11T13:48:33.804Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/csv-json-tablo"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/csv-json-tablo"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/csv-json-tablo"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551836022-deb4988cc6c0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tablo veriyi Claude&apos;la güvenle işle: temizleme, dönüştürme, doğrulama, kontrol kodu üretme ve sonucu doğrulamanın akışı.</image:caption>
      <image:title>CSV, JSON ve Tablolarla Çalışma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/csv-json-tablo</loc>
    <lastmod>2026-05-11T13:48:33.804Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/csv-json-tablo"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/csv-json-tablo"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/csv-json-tablo"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551836022-deb4988cc6c0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tablo veriyi Claude&apos;la güvenle işle: temizleme, dönüştürme, doğrulama, kontrol kodu üretme ve sonucu doğrulamanın akışı.</image:caption>
      <image:title>CSV, JSON ve Tablolarla Çalışma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/istatistik-yorumlama</loc>
    <lastmod>2026-05-11T13:48:33.912Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/istatistik-yorumlama"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/istatistik-yorumlama"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/istatistik-yorumlama"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sayılar değil hikâyeler — Claude&apos;la istatistiksel sonuçları yorumla, anlamlılık eşiği, görselleştirme önerileri.</image:caption>
      <image:title>İstatistiksel Yorumlama ve Görselleştirme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/istatistik-yorumlama</loc>
    <lastmod>2026-05-11T13:48:33.912Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/istatistik-yorumlama"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/istatistik-yorumlama"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/istatistik-yorumlama"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sayılar değil hikâyeler — Claude&apos;la istatistiksel sonuçları yorumla, anlamlılık eşiği, görselleştirme önerileri.</image:caption>
      <image:title>İstatistiksel Yorumlama ve Görselleştirme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/uzun-belge-ozetleme</loc>
    <lastmod>2026-05-11T13:48:34.026Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/uzun-belge-ozetleme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/uzun-belge-ozetleme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/uzun-belge-ozetleme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>200 sayfalık raporlardan 1 sayfalık brief&apos;e: map-reduce, anchored summarization ve faithfulness eval.</image:caption>
      <image:title>Uzun Belgeleri Özetleme ve Sentez</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/uzun-belge-ozetleme</loc>
    <lastmod>2026-05-11T13:48:34.026Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/uzun-belge-ozetleme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/uzun-belge-ozetleme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/uzun-belge-ozetleme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>200 sayfalık raporlardan 1 sayfalık brief&apos;e: map-reduce, anchored summarization ve faithfulness eval.</image:caption>
      <image:title>Uzun Belgeleri Özetleme ve Sentez</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/karsilastirmali-analiz</loc>
    <lastmod>2026-05-11T13:48:34.112Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/karsilastirmali-analiz"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/karsilastirmali-analiz"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/karsilastirmali-analiz"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Birbiriyle çelişen kaynakları birlikte tartmanın yolu: kaynak güvenilirlik etiketi, çelişki haritası, sentez bias kontrolü.</image:caption>
      <image:title>Karşılaştırmalı Analiz ve Çoklu Kaynak Değerlendirme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/karsilastirmali-analiz</loc>
    <lastmod>2026-05-11T13:48:34.112Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/karsilastirmali-analiz"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/karsilastirmali-analiz"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/karsilastirmali-analiz"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Birbiriyle çelişen kaynakları birlikte tartmanın yolu: kaynak güvenilirlik etiketi, çelişki haritası, sentez bias kontrolü.</image:caption>
      <image:title>Karşılaştırmalı Analiz ve Çoklu Kaynak Değerlendirme</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/halusinasyon-kaynak-dogrulama</loc>
    <lastmod>2026-05-11T13:48:34.202Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/halusinasyon-kaynak-dogrulama"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/halusinasyon-kaynak-dogrulama"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/halusinasyon-kaynak-dogrulama"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Hallüsinasyonu sıfırlayamazsın ama yakalayabilirsin. 6 teknikle Claude çıktısını doğrulamayı sistemleştir.</image:caption>
      <image:title>Hallüsinasyonu Yakalama ve Kaynak Doğrulama</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/halusinasyon-kaynak-dogrulama</loc>
    <lastmod>2026-05-11T13:48:34.202Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/halusinasyon-kaynak-dogrulama"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/halusinasyon-kaynak-dogrulama"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/halusinasyon-kaynak-dogrulama"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Hallüsinasyonu sıfırlayamazsın ama yakalayabilirsin. 6 teknikle Claude çıktısını doğrulamayı sistemleştir.</image:caption>
      <image:title>Hallüsinasyonu Yakalama ve Kaynak Doğrulama</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/tool-use</loc>
    <lastmod>2026-05-13T11:38:19.503Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/tool-use"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/tool-use"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/tool-use"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551434678-e076c223a692?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude&apos;a hesap makinesi, veritabanı, e-posta, Slack, kod sandbox gibi araçları nasıl tanıttırırsın? Tool use&apos;un anatomisi ve üretim kalıbı.</image:caption>
      <image:title>Tool Use: Claude&apos;a Yetenek Eklemek</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/tool-use</loc>
    <lastmod>2026-05-13T11:38:19.503Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/tool-use"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/tool-use"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/tool-use"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551434678-e076c223a692?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude&apos;a hesap makinesi, veritabanı, e-posta, Slack, kod sandbox gibi araçları nasıl tanıttırırsın? Tool use&apos;un anatomisi ve üretim kalıbı.</image:caption>
      <image:title>Tool Use: Claude&apos;a Yetenek Eklemek</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/vision</loc>
    <lastmod>2026-05-11T13:48:34.395Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/vision"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/vision"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/vision"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Ekran görüntüsü, fotoğraf, grafik, el yazısı not — Claude vision ile görselden bilgi çıkarmanın akışı ve sınırları.</image:caption>
      <image:title>Vision: Görsel Anlama ve Analiz</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/vision</loc>
    <lastmod>2026-05-11T13:48:34.395Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/vision"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/vision"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/vision"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Ekran görüntüsü, fotoğraf, grafik, el yazısı not — Claude vision ile görselden bilgi çıkarmanın akışı ve sınırları.</image:caption>
      <image:title>Vision: Görsel Anlama ve Analiz</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/pdf-belge</loc>
    <lastmod>2026-05-11T13:48:34.488Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/pdf-belge"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/pdf-belge"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/pdf-belge"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>PDF&apos;leri Claude&apos;a vermek, çok sayfalı belgelerden veri çıkarmak ve form / sözleşme analizinin akışı.</image:caption>
      <image:title>PDF ve Belge İşleme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/pdf-belge</loc>
    <lastmod>2026-05-11T13:48:34.488Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/pdf-belge"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/pdf-belge"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/pdf-belge"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>PDF&apos;leri Claude&apos;a vermek, çok sayfalı belgelerden veri çıkarmak ve form / sözleşme analizinin akışı.</image:caption>
      <image:title>PDF ve Belge İşleme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/computer-use</loc>
    <lastmod>2026-05-13T11:39:45.950Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/computer-use"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/computer-use"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/computer-use"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude&apos;un ekranını, tarayıcısını ve fare/klavyeyi kontrol ettiği iki yetenek: Computer Use ve Claude in Chrome. Güvenli kullanım pratikleri dahil.</image:caption>
      <image:title>Computer Use: Ekran ve Tarayıcı Kontrolü</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/computer-use</loc>
    <lastmod>2026-05-13T11:39:45.950Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/computer-use"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/computer-use"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/computer-use"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude&apos;un ekranını, tarayıcısını ve fare/klavyeyi kontrol ettiği iki yetenek: Computer Use ve Claude in Chrome. Güvenli kullanım pratikleri dahil.</image:caption>
      <image:title>Computer Use: Ekran ve Tarayıcı Kontrolü</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/extended-thinking</loc>
    <lastmod>2026-05-11T13:48:34.696Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/extended-thinking"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/extended-thinking"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/extended-thinking"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Karmaşık görevlerde modelin gizli düşünce alanını açmak: extended thinking modu nedir, ne zaman aç, maliyeti nedir?</image:caption>
      <image:title>Extended Thinking: Uzun Düşünme Modu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/extended-thinking</loc>
    <lastmod>2026-05-11T13:48:34.696Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/extended-thinking"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/extended-thinking"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/extended-thinking"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Karmaşık görevlerde modelin gizli düşünce alanını açmak: extended thinking modu nedir, ne zaman aç, maliyeti nedir?</image:caption>
      <image:title>Extended Thinking: Uzun Düşünme Modu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/artifacts</loc>
    <lastmod>2026-05-11T13:48:34.794Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/artifacts"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/artifacts"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/artifacts"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude&apos;un ürettiği kod, SVG veya React bileşeninin yan panelde canlı render olduğu Artifacts mekaniği. Üretim ve canlı iyileştirme.</image:caption>
      <image:title>Artifacts: Anında Çalışan Çıktılar</image:title>
    </image:image>
  </url>
  <url>
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    <lastmod>2026-05-11T13:48:34.794Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/artifacts"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/artifacts"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/artifacts"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Claude&apos;un ürettiği kod, SVG veya React bileşeninin yan panelde canlı render olduğu Artifacts mekaniği. Üretim ve canlı iyileştirme.</image:caption>
      <image:title>Artifacts: Anında Çalışan Çıktılar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/api-baslangic</loc>
    <lastmod>2026-05-11T13:48:34.889Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/api-baslangic"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/api-baslangic"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/api-baslangic"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1531297484001-80022131f5a1?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anthropic console&apos;dan API key al, SDK kur, ilk Claude çağrısını yap. Python ve TypeScript adım adım.</image:caption>
      <image:title>API&apos;ye Başlangıç: Auth, İlk İstek, SDK Kurulumu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/api-baslangic</loc>
    <lastmod>2026-05-11T13:48:34.889Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/api-baslangic"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/api-baslangic"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/api-baslangic"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1531297484001-80022131f5a1?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anthropic console&apos;dan API key al, SDK kur, ilk Claude çağrısını yap. Python ve TypeScript adım adım.</image:caption>
      <image:title>API&apos;ye Başlangıç: Auth, İlk İstek, SDK Kurulumu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/messages-api</loc>
    <lastmod>2026-05-11T13:48:34.966Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/messages-api"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/messages-api"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/messages-api"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Messages API&apos;nin yapı taşları, sistem mesajı, çok turlu konuşma, response object&apos;inin yapısı ve token sayma.</image:caption>
      <image:title>Messages API: Çok Turlu Konuşmalar</image:title>
    </image:image>
  </url>
  <url>
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    <lastmod>2026-05-11T13:48:34.966Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/messages-api"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/messages-api"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/messages-api"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Messages API&apos;nin yapı taşları, sistem mesajı, çok turlu konuşma, response object&apos;inin yapısı ve token sayma.</image:caption>
      <image:title>Messages API: Çok Turlu Konuşmalar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/streaming</loc>
    <lastmod>2026-05-11T13:48:35.060Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/streaming"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/streaming"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/streaming"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1542903660-eedba2cda473?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Cevabı yazılırken kullanıcıya gösteren streaming nedir, neden önemlidir, SSE / Web Streams ile entegrasyon.</image:caption>
      <image:title>Streaming Yanıtlar ve Real-Time UX</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/streaming</loc>
    <lastmod>2026-05-11T13:48:35.060Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/streaming"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/streaming"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/streaming"/>
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      <image:loc>https://images.unsplash.com/photo-1542903660-eedba2cda473?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Cevabı yazılırken kullanıcıya gösteren streaming nedir, neden önemlidir, SSE / Web Streams ile entegrasyon.</image:caption>
      <image:title>Streaming Yanıtlar ve Real-Time UX</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/tool-use-api</loc>
    <lastmod>2026-05-11T13:48:35.152Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/tool-use-api"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/tool-use-api"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/tool-use-api"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1531297484001-80022131f5a1?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 7&apos;deki tool use&apos;u API üzerinden bitir: tam loop, paralel tool, error feedback ve schema doğrulama.</image:caption>
      <image:title>Tool Use API: Fonksiyon Çağırma Pratiği</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/tool-use-api</loc>
    <lastmod>2026-05-11T13:48:35.152Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/tool-use-api"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/tool-use-api"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/tool-use-api"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1531297484001-80022131f5a1?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 7&apos;deki tool use&apos;u API üzerinden bitir: tam loop, paralel tool, error feedback ve schema doğrulama.</image:caption>
      <image:title>Tool Use API: Fonksiyon Çağırma Pratiği</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/prompt-caching</loc>
    <lastmod>2026-05-11T13:48:35.261Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/prompt-caching"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/prompt-caching"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/prompt-caching"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sabit sistem promptu, büyük few-shot blokları ve uzun belgeleri cache&apos;leyerek input maliyetini büyük oranda düşür.</image:caption>
      <image:title>Prompt Caching ile %90&apos;a Kadar Maliyet Düşürme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/prompt-caching</loc>
    <lastmod>2026-05-11T13:48:35.261Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/prompt-caching"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/prompt-caching"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/prompt-caching"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sabit sistem promptu, büyük few-shot blokları ve uzun belgeleri cache&apos;leyerek input maliyetini büyük oranda düşür.</image:caption>
      <image:title>Prompt Caching ile %90&apos;a Kadar Maliyet Düşürme</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/batch-api</loc>
    <lastmod>2026-05-11T13:48:35.358Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/batch-api"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/batch-api"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/batch-api"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anlık olmayan iş yüklerini batch API ile %50 ucuz çalıştır. Etiketleme, içerik üretimi, eval&apos;lar için ideal.</image:caption>
      <image:title>Batch API: Toplu İşlemler ve Async</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/batch-api</loc>
    <lastmod>2026-05-11T13:48:35.358Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/batch-api"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/batch-api"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/batch-api"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anlık olmayan iş yüklerini batch API ile %50 ucuz çalıştır. Etiketleme, içerik üretimi, eval&apos;lar için ideal.</image:caption>
      <image:title>Batch API: Toplu İşlemler ve Async</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/error-handling</loc>
    <lastmod>2026-05-11T13:48:35.448Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/error-handling"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/error-handling"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/error-handling"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1532153975070-2e9ab71f1b14?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>API hatalarını sınıflandır, doğru retry stratejisini uygula, idempotency key kullan, dead-letter queue tasarla.</image:caption>
      <image:title>Hata Yönetimi, Rate Limit ve Retry Stratejileri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/error-handling</loc>
    <lastmod>2026-05-11T13:48:35.448Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/error-handling"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/error-handling"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/error-handling"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1532153975070-2e9ab71f1b14?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>API hatalarını sınıflandır, doğru retry stratejisini uygula, idempotency key kullan, dead-letter queue tasarla.</image:caption>
      <image:title>Hata Yönetimi, Rate Limit ve Retry Stratejileri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/evals</loc>
    <lastmod>2026-05-11T13:48:35.532Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/evals"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/evals"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/evals"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Üretim kalitesini ölçen eval setleri tasarlamak: oluşturma, dengeleme, otomatik puanlama (LLM-as-judge), insan kalibrasyonu.</image:caption>
      <image:title>Eval Setleri ve LLM-as-Judge</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/evals</loc>
    <lastmod>2026-05-11T13:48:35.532Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/evals"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/evals"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/evals"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Üretim kalitesini ölçen eval setleri tasarlamak: oluşturma, dengeleme, otomatik puanlama (LLM-as-judge), insan kalibrasyonu.</image:caption>
      <image:title>Eval Setleri ve LLM-as-Judge</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/security</loc>
    <lastmod>2026-05-11T13:48:35.625Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/security"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/security"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/security"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Saldırgan kullanıcı, kötü niyetli içerik veya manipüle edilmiş veri Claude&apos;u nasıl etkiler? Sekiz savunma kalıbı.</image:caption>
      <image:title>Prompt Injection, Jailbreak ve Savunma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/security</loc>
    <lastmod>2026-05-11T13:48:35.625Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/security"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/security"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/security"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Saldırgan kullanıcı, kötü niyetli içerik veya manipüle edilmiş veri Claude&apos;u nasıl etkiler? Sekiz savunma kalıbı.</image:caption>
      <image:title>Prompt Injection, Jailbreak ve Savunma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/cost-monitoring</loc>
    <lastmod>2026-05-11T13:48:35.737Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/cost-monitoring"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/cost-monitoring"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/cost-monitoring"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Beklenmeyen fatura sürprizine düşmemek için maliyet pipeline&apos;ı: per-user kota, alarm eşikleri, anomaly detection.</image:caption>
      <image:title>Maliyet İzleme, Kota ve Bütçe Alarmları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/cost-monitoring</loc>
    <lastmod>2026-05-11T13:48:35.737Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/cost-monitoring"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/cost-monitoring"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/cost-monitoring"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Beklenmeyen fatura sürprizine düşmemek için maliyet pipeline&apos;ı: per-user kota, alarm eşikleri, anomaly detection.</image:caption>
      <image:title>Maliyet İzleme, Kota ve Bütçe Alarmları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/performance</loc>
    <lastmod>2026-05-11T13:48:35.825Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/performance"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/performance"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/performance"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>p50 / p95 / p99 latency&apos;i düşürmek için 8 kaldırac: model seçimi, cache, streaming, parallelism.</image:caption>
      <image:title>Latency, Caching ve Performans Optimizasyonu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/performance</loc>
    <lastmod>2026-05-11T13:48:35.825Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/performance"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/performance"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/performance"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>p50 / p95 / p99 latency&apos;i düşürmek için 8 kaldırac: model seçimi, cache, streaming, parallelism.</image:caption>
      <image:title>Latency, Caching ve Performans Optimizasyonu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/monitoring</loc>
    <lastmod>2026-05-11T13:48:35.943Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/monitoring"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/monitoring"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/monitoring"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>OpenTelemetry uyumlu LLM tracing, structured log şeması, hata bildirimi, prompt versiyonlu izleme.</image:caption>
      <image:title>Logging, Tracing ve Observability</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/monitoring</loc>
    <lastmod>2026-05-11T13:48:35.943Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/monitoring"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/monitoring"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/monitoring"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>OpenTelemetry uyumlu LLM tracing, structured log şeması, hata bildirimi, prompt versiyonlu izleme.</image:caption>
      <image:title>Logging, Tracing ve Observability</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/agent-temelleri</loc>
    <lastmod>2026-05-11T13:48:36.047Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-temelleri"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/agent-temelleri"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-temelleri"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551434678-e076c223a692?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Agent ile pipeline farkı, agent&apos;ın 4 yapı taşı (planner, memory, tools, controller) ve hangi problem agent gerektirir?</image:caption>
      <image:title>Agent Nedir? Reaktif vs Otonom Sistemler</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/agent-temelleri</loc>
    <lastmod>2026-05-11T13:48:36.047Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-temelleri"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/agent-temelleri"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-temelleri"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551434678-e076c223a692?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Agent ile pipeline farkı, agent&apos;ın 4 yapı taşı (planner, memory, tools, controller) ve hangi problem agent gerektirir?</image:caption>
      <image:title>Agent Nedir? Reaktif vs Otonom Sistemler</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/agent-sdk</loc>
    <lastmod>2026-05-11T13:48:36.143Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-sdk"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/agent-sdk"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-sdk"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anthropic&apos;in Agent SDK&apos;sı ile Hello-World agent&apos;ı: tool tanımı, sistem prompt&apos;u, controller loop ve insan onayı.</image:caption>
      <image:title>Claude Agent SDK ile İlk Agent</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/agent-sdk</loc>
    <lastmod>2026-05-11T13:48:36.143Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-sdk"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/agent-sdk"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-sdk"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anthropic&apos;in Agent SDK&apos;sı ile Hello-World agent&apos;ı: tool tanımı, sistem prompt&apos;u, controller loop ve insan onayı.</image:caption>
      <image:title>Claude Agent SDK ile İlk Agent</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/agent-multi-tool</loc>
    <lastmod>2026-05-11T13:48:36.227Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-multi-tool"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/agent-multi-tool"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-multi-tool"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1531746790731-6c087fecd65a?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Karmaşık görevlerde planner + executor ayrımı; tool seçimi rehberi; iç içe agent çağrısı (sub-agent).</image:caption>
      <image:title>Çoklu Araçlı Agent Mimarileri (Planner-Executor)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/agent-multi-tool</loc>
    <lastmod>2026-05-11T13:48:36.227Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-multi-tool"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/agent-multi-tool"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-multi-tool"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1531746790731-6c087fecd65a?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Karmaşık görevlerde planner + executor ayrımı; tool seçimi rehberi; iç içe agent çağrısı (sub-agent).</image:caption>
      <image:title>Çoklu Araçlı Agent Mimarileri (Planner-Executor)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/agent-memory</loc>
    <lastmod>2026-05-11T13:48:36.330Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-memory"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/agent-memory"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-memory"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Çoklu adımlı / çoklu oturumlu agent&apos;larda hafıza katmanları: scratch, episodic, semantic, kullanıcı profili.</image:caption>
      <image:title>Hafıza, Durum ve Uzun Vadeli Bağlam</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/agent-memory</loc>
    <lastmod>2026-05-11T13:48:36.330Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-memory"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/agent-memory"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-memory"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Çoklu adımlı / çoklu oturumlu agent&apos;larda hafıza katmanları: scratch, episodic, semantic, kullanıcı profili.</image:caption>
      <image:title>Hafıza, Durum ve Uzun Vadeli Bağlam</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/agent-hitl</loc>
    <lastmod>2026-05-11T13:48:36.413Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-hitl"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/agent-hitl"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-hitl"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1531746790731-6c087fecd65a?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Riskli agent eylemlerini insan onayına bağlamak: pre-execution diff, severity tier, audit log.</image:caption>
      <image:title>Human-in-the-Loop ve Onay Akışları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/agent-hitl</loc>
    <lastmod>2026-05-11T13:48:36.413Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-hitl"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/agent-hitl"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/agent-hitl"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1531746790731-6c087fecd65a?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Riskli agent eylemlerini insan onayına bağlamak: pre-execution diff, severity tier, audit log.</image:caption>
      <image:title>Human-in-the-Loop ve Onay Akışları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/support-bot-projesi</loc>
    <lastmod>2026-05-11T13:48:36.511Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/support-bot-projesi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/support-bot-projesi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/support-bot-projesi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>TR/EN destek pipeline&apos;ı: niyet, FAQ, eskalasyon, CSAT geri besleme. Hangi adım hangi modelde, eval seti nasıl tasarlanır?</image:caption>
      <image:title>Proje: Çok Dilli Müşteri Destek Asistanı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/support-bot-projesi</loc>
    <lastmod>2026-05-11T13:48:36.511Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/support-bot-projesi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/support-bot-projesi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/support-bot-projesi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>TR/EN destek pipeline&apos;ı: niyet, FAQ, eskalasyon, CSAT geri besleme. Hangi adım hangi modelde, eval seti nasıl tasarlanır?</image:caption>
      <image:title>Proje: Çok Dilli Müşteri Destek Asistanı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/rag-projesi</loc>
    <lastmod>2026-05-11T13:48:36.606Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/rag-projesi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/rag-projesi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/rag-projesi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Şirket dokümantasyonu üzerinde RAG: chunking, embedding, retrieval, re-ranking, anchored answer.</image:caption>
      <image:title>Proje: RAG ile Doküman Sorgulama Sistemi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/rag-projesi</loc>
    <lastmod>2026-05-11T13:48:36.606Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/rag-projesi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/rag-projesi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/rag-projesi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Şirket dokümantasyonu üzerinde RAG: chunking, embedding, retrieval, re-ranking, anchored answer.</image:caption>
      <image:title>Proje: RAG ile Doküman Sorgulama Sistemi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/code-review-projesi</loc>
    <lastmod>2026-05-11T13:48:36.690Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/code-review-projesi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/code-review-projesi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/code-review-projesi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GitHub Actions + Claude&apos;la PR review otomasyonu: diff parse, kural setine göre yorum, severity etiketi.</image:caption>
      <image:title>Proje: Kod İnceleme Otomasyonu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/code-review-projesi</loc>
    <lastmod>2026-05-11T13:48:36.690Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/code-review-projesi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/code-review-projesi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/code-review-projesi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GitHub Actions + Claude&apos;la PR review otomasyonu: diff parse, kural setine göre yorum, severity etiketi.</image:caption>
      <image:title>Proje: Kod İnceleme Otomasyonu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/content-pipeline-projesi</loc>
    <lastmod>2026-05-11T13:48:36.791Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/content-pipeline-projesi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/content-pipeline-projesi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/content-pipeline-projesi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Konu listesinden yayına: outline → draft → editör → SEO → görsel önerisi → CMS yayını. Insan onayı her adımda.</image:caption>
      <image:title>Proje: İçerik Üretim Pipeline&apos;ı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/content-pipeline-projesi</loc>
    <lastmod>2026-05-11T13:48:36.791Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/content-pipeline-projesi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/content-pipeline-projesi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/content-pipeline-projesi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Konu listesinden yayına: outline → draft → editör → SEO → görsel önerisi → CMS yayını. Insan onayı her adımda.</image:caption>
      <image:title>Proje: İçerik Üretim Pipeline&apos;ı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/claude-ustaligi/extraction-projesi</loc>
    <lastmod>2026-05-11T13:48:36.880Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/extraction-projesi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/extraction-projesi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/extraction-projesi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Faturalar, sözleşmeler, formlar — Claude vision + tool use ile yapısal veri çıkarımı. Doğruluk metrikleri ve audit trail.</image:caption>
      <image:title>Proje: PDF&apos;ten Yapılandırılmış Veri Çıkarımı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/claude-ustaligi/extraction-projesi</loc>
    <lastmod>2026-05-11T13:48:36.880Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/claude-ustaligi/extraction-projesi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/claude-ustaligi/extraction-projesi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/claude-ustaligi/extraction-projesi"/>
    <image:image>
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      <image:caption>Faturalar, sözleşmeler, formlar — Claude vision + tool use ile yapısal veri çıkarımı. Doğruluk metrikleri ve audit trail.</image:caption>
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      <image:caption>Kursun toplam tahmini maliyeti üç senaryoda, token economics 101, bütçe alarmı kurma, AI mühendisinin etik sözleşmesi: telif hakları, KVKK, EU AI Act, akademik dürüstlük, açık-kaynak katkı, çevre etkisi.</image:caption>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/turev-gradient-matrix-calculus-backprop"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/turev-gradient-matrix-calculus-backprop"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Skalerden vektöre, vektörden matrise türev. Jacobian, Hessian, chain rule, numerator vs denominator layout. Softmax + cross-entropy&apos;nin türevinin neden zarif olduğu. Backprop&apos;un manuel hesabıyla PyTorch autograd karşılaştırması.</image:caption>
      <image:title>Türev, Gradient ve Matrix Calculus: Backprop&apos;un Matematiği Sıfırdan</image:title>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/turev-gradient-matrix-calculus-backprop</loc>
    <lastmod>2026-05-13T13:00:22.832Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/turev-gradient-matrix-calculus-backprop"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/turev-gradient-matrix-calculus-backprop"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/turev-gradient-matrix-calculus-backprop"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Skalerden vektöre, vektörden matrise türev. Jacobian, Hessian, chain rule, numerator vs denominator layout. Softmax + cross-entropy&apos;nin türevinin neden zarif olduğu. Backprop&apos;un manuel hesabıyla PyTorch autograd karşılaştırması.</image:caption>
      <image:title>Türev, Gradient ve Matrix Calculus: Backprop&apos;un Matematiği Sıfırdan</image:title>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/chain-rule-backpropagation-mini-autograd</loc>
    <lastmod>2026-05-13T13:00:22.922Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/chain-rule-backpropagation-mini-autograd"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/chain-rule-backpropagation-mini-autograd"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/chain-rule-backpropagation-mini-autograd"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Karpathy&apos;nin micrograd&apos;ını Türkçe sıfırdan inşa etmek — 200 satır PyTorch-benzeri otomatik türev motoru. Computational graph, topological sort, operator overloading, _backward closures, gradient accumulation. Sonunda bir MLP&apos;yi eğit.</image:caption>
      <image:title>Chain Rule ve Backpropagation: Mini-Autograd&apos;ı Sıfırdan İnşa Et (Karpathy micrograd Türkçe)</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/chain-rule-backpropagation-mini-autograd</loc>
    <lastmod>2026-05-13T13:00:22.922Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/chain-rule-backpropagation-mini-autograd"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/chain-rule-backpropagation-mini-autograd"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/chain-rule-backpropagation-mini-autograd"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Karpathy&apos;nin micrograd&apos;ını Türkçe sıfırdan inşa etmek — 200 satır PyTorch-benzeri otomatik türev motoru. Computational graph, topological sort, operator overloading, _backward closures, gradient accumulation. Sonunda bir MLP&apos;yi eğit.</image:caption>
      <image:title>Chain Rule ve Backpropagation: Mini-Autograd&apos;ı Sıfırdan İnşa Et (Karpathy micrograd Türkçe)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/olasilik-temelleri-joint-marginal-conditional-bayes</loc>
    <lastmod>2026-05-13T13:00:23.014Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/olasilik-temelleri-joint-marginal-conditional-bayes"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/olasilik-temelleri-joint-marginal-conditional-bayes"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/olasilik-temelleri-joint-marginal-conditional-bayes"/>
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      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM&apos;ler özünde conditional probability makineleridir. P(x_t | x_&lt;t)&apos;nin matematiği, joint/marginal/conditional ilişkisi, bağımsızlık, Bayes teoreminin gücü, dağılım aileleri (Bernoulli, Categorical, Gaussian), expectation, variance — sampling (temperature, top-k, top-p) buradan başlar.</image:caption>
      <image:title>Olasılık Temelleri: Joint, Marginal, Conditional ve Bayes — LLM&apos;in Düşünme Dili</image:title>
    </image:image>
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    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/olasilik-temelleri-joint-marginal-conditional-bayes</loc>
    <lastmod>2026-05-13T13:00:23.014Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/olasilik-temelleri-joint-marginal-conditional-bayes"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/olasilik-temelleri-joint-marginal-conditional-bayes"/>
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      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM&apos;ler özünde conditional probability makineleridir. P(x_t | x_&lt;t)&apos;nin matematiği, joint/marginal/conditional ilişkisi, bağımsızlık, Bayes teoreminin gücü, dağılım aileleri (Bernoulli, Categorical, Gaussian), expectation, variance — sampling (temperature, top-k, top-p) buradan başlar.</image:caption>
      <image:title>Olasılık Temelleri: Joint, Marginal, Conditional ve Bayes — LLM&apos;in Düşünme Dili</image:title>
    </image:image>
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    <lastmod>2026-05-13T13:00:23.105Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/mle-map-posterior-modelleme-grameri"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/mle-map-posterior-modelleme-grameri"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/mle-map-posterior-modelleme-grameri"/>
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      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM pretrain loss&apos;unun bir Maximum Likelihood Estimation (MLE) objective olduğunu, fine-tuning&apos;in matematiksel olarak Bayesian güncelleme olduğunu, regularization&apos;ın MAP&apos;a karşılık geldiğini gözden geçir. Cross-entropy = NLL ilişkisi, prior seçimi, conjugate priors.</image:caption>
      <image:title>MLE, MAP, Posterior: Modelleme Dilinin Grameri — Pretrain Loss&apos;un Matematiksel Kökü</image:title>
    </image:image>
  </url>
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    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/mle-map-posterior-modelleme-grameri</loc>
    <lastmod>2026-05-13T13:00:23.105Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/mle-map-posterior-modelleme-grameri"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/mle-map-posterior-modelleme-grameri"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/mle-map-posterior-modelleme-grameri"/>
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      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM pretrain loss&apos;unun bir Maximum Likelihood Estimation (MLE) objective olduğunu, fine-tuning&apos;in matematiksel olarak Bayesian güncelleme olduğunu, regularization&apos;ın MAP&apos;a karşılık geldiğini gözden geçir. Cross-entropy = NLL ilişkisi, prior seçimi, conjugate priors.</image:caption>
      <image:title>MLE, MAP, Posterior: Modelleme Dilinin Grameri — Pretrain Loss&apos;un Matematiksel Kökü</image:title>
    </image:image>
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    <lastmod>2026-05-13T13:00:23.193Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/entropi-kl-divergence-mutual-information"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/entropi-kl-divergence-mutual-information"/>
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      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Shannon entropisi, cross-entropy&apos;nin LLM loss olarak gerçek anlamı, KL divergence&apos;ın asimetrisi ve forward vs reverse KL (mode covering vs mode seeking), RLHF/DPO&apos;da KL constraint&apos;in rolü, JS ve Wasserstein, mutual information, knowledge distillation matematik.</image:caption>
      <image:title>Entropi, Cross-Entropy, KL Divergence ve Mutual Information: Bilgi Teorisinin LLM&apos;deki Hayatı</image:title>
    </image:image>
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    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/entropi-kl-divergence-mutual-information</loc>
    <lastmod>2026-05-13T13:00:23.193Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/entropi-kl-divergence-mutual-information"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/entropi-kl-divergence-mutual-information"/>
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      <image:caption>Shannon entropisi, cross-entropy&apos;nin LLM loss olarak gerçek anlamı, KL divergence&apos;ın asimetrisi ve forward vs reverse KL (mode covering vs mode seeking), RLHF/DPO&apos;da KL constraint&apos;in rolü, JS ve Wasserstein, mutual information, knowledge distillation matematik.</image:caption>
      <image:title>Entropi, Cross-Entropy, KL Divergence ve Mutual Information: Bilgi Teorisinin LLM&apos;deki Hayatı</image:title>
    </image:image>
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    <lastmod>2026-05-13T13:00:23.285Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/optimization-sgd-adam-adamw-lion-muon"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/optimization-sgd-adam-adamw-lion-muon"/>
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      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Gradient descent ailesinin geçmişi ve geleceği: GD, SGD, Momentum (Heavy ball, Nesterov), AdaGrad, RMSProp, Adam, AdamW, Lion, Muon. Learning rate schedules: linear warmup + cosine decay. Loss landscape: sharp vs flat minima.</image:caption>
      <image:title>Optimization: SGD&apos;den AdamW&apos;a, Lion&apos;a, Muon&apos;a — Modern LLM&apos;in Tüm Optimizer&apos;ları</image:title>
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    <lastmod>2026-05-13T13:00:23.285Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/optimization-sgd-adam-adamw-lion-muon"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/optimization-sgd-adam-adamw-lion-muon"/>
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      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Gradient descent ailesinin geçmişi ve geleceği: GD, SGD, Momentum (Heavy ball, Nesterov), AdaGrad, RMSProp, Adam, AdamW, Lion, Muon. Learning rate schedules: linear warmup + cosine decay. Loss landscape: sharp vs flat minima.</image:caption>
      <image:title>Optimization: SGD&apos;den AdamW&apos;a, Lion&apos;a, Muon&apos;a — Modern LLM&apos;in Tüm Optimizer&apos;ları</image:title>
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    <lastmod>2026-05-13T13:00:23.372Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/numerik-stabilite-fp16-bf16-fp8-nan"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/numerik-stabilite-fp16-bf16-fp8-nan"/>
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      <image:caption>Floating point representation (FP32, FP16, BF16, FP8), overflow/underflow/NaN avı, log-sum-exp trick, softmax sayısal stabilitesi, mixed precision training (autocast + GradScaler), pretrain loss spike&apos;larının sayısal kökenleri.</image:caption>
      <image:title>Numerik Stabilite: Log-Sum-Exp, FP16 Tuzakları, NaN Avı — LLM Eğitiminin Gizli Saatleri</image:title>
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    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/numerik-stabilite-fp16-bf16-fp8-nan</loc>
    <lastmod>2026-05-13T13:00:23.372Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/numerik-stabilite-fp16-bf16-fp8-nan"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/numerik-stabilite-fp16-bf16-fp8-nan"/>
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      <image:caption>Floating point representation (FP32, FP16, BF16, FP8), overflow/underflow/NaN avı, log-sum-exp trick, softmax sayısal stabilitesi, mixed precision training (autocast + GradScaler), pretrain loss spike&apos;larının sayısal kökenleri.</image:caption>
      <image:title>Numerik Stabilite: Log-Sum-Exp, FP16 Tuzakları, NaN Avı — LLM Eğitiminin Gizli Saatleri</image:title>
    </image:image>
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    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
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    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/bilgi-geometrisi-manifold-embedding"/>
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      <image:caption>Embedding space&apos;in geometrik anatomisi: manifold hipotezi, t-SNE/UMAP görselleştirme, cosine vs Euclidean metric, Riemannian geometri sezgisi, Fisher information, natural gradient, embedding rotation invariance. Bu dersle Modül 1&apos;i tamamlıyoruz.</image:caption>
      <image:title>Bilgi Geometrisi ve Manifold Sezgisi: Embedding&apos;lerin Niçin Anlamlı Olduğu</image:title>
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      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Embedding space&apos;in geometrik anatomisi: manifold hipotezi, t-SNE/UMAP görselleştirme, cosine vs Euclidean metric, Riemannian geometri sezgisi, Fisher information, natural gradient, embedding rotation invariance. Bu dersle Modül 1&apos;i tamamlıyoruz.</image:caption>
      <image:title>Bilgi Geometrisi ve Manifold Sezgisi: Embedding&apos;lerin Niçin Anlamlı Olduğu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/numpy-tensor-strides-view-broadcasting</loc>
    <lastmod>2026-05-13T13:00:23.545Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/numpy-tensor-strides-view-broadcasting"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/numpy-tensor-strides-view-broadcasting"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/numpy-tensor-strides-view-broadcasting"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir tensor&apos;un bellek anatomisi: row-major C vs column-major F, strides, view vs copy, contiguous, fancy indexing, advanced broadcasting kuralları, BLAS arka uç sezgisi, einsum vs einops. Performans kritik kodun temeli.</image:caption>
      <image:title>NumPy Tensor Mühendisliği: Strides, View, Broadcasting ve Bellek Düzeninin Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/numpy-tensor-strides-view-broadcasting</loc>
    <lastmod>2026-05-13T13:00:23.545Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/numpy-tensor-strides-view-broadcasting"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/numpy-tensor-strides-view-broadcasting"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/numpy-tensor-strides-view-broadcasting"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir tensor&apos;un bellek anatomisi: row-major C vs column-major F, strides, view vs copy, contiguous, fancy indexing, advanced broadcasting kuralları, BLAS arka uç sezgisi, einsum vs einops. Performans kritik kodun temeli.</image:caption>
      <image:title>NumPy Tensor Mühendisliği: Strides, View, Broadcasting ve Bellek Düzeninin Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/computational-graph-dag-topological-eager-static</loc>
    <lastmod>2026-05-13T13:00:23.633Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/computational-graph-dag-topological-eager-static"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/computational-graph-dag-topological-eager-static"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/computational-graph-dag-topological-eager-static"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Autograd&apos;in arkasındaki graph yapısının derinlemesine analizi: DAG anatomisi, in-degree/out-degree, topological sort algoritmaları (DFS post-order, Kahn&apos;s), eager (PyTorch) vs static (TF1, JAX, XLA) graph paradigmaları, graph optimization (fusion, dead code elimination).</image:caption>
      <image:title>Computational Graph Derinden: DAG Yapısı, Topological Sort, Eager vs Static Paradigma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/computational-graph-dag-topological-eager-static</loc>
    <lastmod>2026-05-13T13:00:23.633Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/computational-graph-dag-topological-eager-static"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/computational-graph-dag-topological-eager-static"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/computational-graph-dag-topological-eager-static"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Autograd&apos;in arkasındaki graph yapısının derinlemesine analizi: DAG anatomisi, in-degree/out-degree, topological sort algoritmaları (DFS post-order, Kahn&apos;s), eager (PyTorch) vs static (TF1, JAX, XLA) graph paradigmaları, graph optimization (fusion, dead code elimination).</image:caption>
      <image:title>Computational Graph Derinden: DAG Yapısı, Topological Sort, Eager vs Static Paradigma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/reverse-mode-forward-mode-autodiff-jvp-vjp</loc>
    <lastmod>2026-05-13T13:00:23.722Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reverse-mode-forward-mode-autodiff-jvp-vjp"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/reverse-mode-forward-mode-autodiff-jvp-vjp"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reverse-mode-forward-mode-autodiff-jvp-vjp"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Otomatik türevin iki temel modu: forward-mode (Jacobian-vector product, dual numbers) ve reverse-mode (vector-Jacobian product, backprop). Matematiksel karşılaştırma, hesaplama karmaşıklığı, JAX&apos;te jvp/vjp/grad/hessian, LLM&apos;de hangi senaryo hangi modu gerektirir.</image:caption>
      <image:title>Reverse-mode vs Forward-mode Autodiff: JVP, VJP, Dual Numbers ve LLM&apos;de Hangisi Ne Zaman</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/reverse-mode-forward-mode-autodiff-jvp-vjp</loc>
    <lastmod>2026-05-13T13:00:23.722Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reverse-mode-forward-mode-autodiff-jvp-vjp"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/reverse-mode-forward-mode-autodiff-jvp-vjp"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reverse-mode-forward-mode-autodiff-jvp-vjp"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Otomatik türevin iki temel modu: forward-mode (Jacobian-vector product, dual numbers) ve reverse-mode (vector-Jacobian product, backprop). Matematiksel karşılaştırma, hesaplama karmaşıklığı, JAX&apos;te jvp/vjp/grad/hessian, LLM&apos;de hangi senaryo hangi modu gerektirir.</image:caption>
      <image:title>Reverse-mode vs Forward-mode Autodiff: JVP, VJP, Dual Numbers ve LLM&apos;de Hangisi Ne Zaman</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/numpy-tensor-autograd-mini-tinygrad</loc>
    <lastmod>2026-05-13T13:00:23.821Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/numpy-tensor-autograd-mini-tinygrad"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/numpy-tensor-autograd-mini-tinygrad"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/numpy-tensor-autograd-mini-tinygrad"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>1.4&apos;teki skaler micrograd&apos;ı tensor seviyesine yükselt: NumPy üzerinde Tensor class, broadcasting-aware backward (sum-along-broadcast-dims trick), matmul/conv/softmax operatörleri, transpose ve view&apos;ın gradient akışı, ~500 satırda PyTorch-benzeri eğitim motoru.</image:caption>
      <image:title>NumPy ile Tensor Autograd Sıfırdan: Broadcasting-Aware Mini-Tinygrad İnşası</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/numpy-tensor-autograd-mini-tinygrad</loc>
    <lastmod>2026-05-13T13:00:23.821Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/numpy-tensor-autograd-mini-tinygrad"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/numpy-tensor-autograd-mini-tinygrad"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/numpy-tensor-autograd-mini-tinygrad"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>1.4&apos;teki skaler micrograd&apos;ı tensor seviyesine yükselt: NumPy üzerinde Tensor class, broadcasting-aware backward (sum-along-broadcast-dims trick), matmul/conv/softmax operatörleri, transpose ve view&apos;ın gradient akışı, ~500 satırda PyTorch-benzeri eğitim motoru.</image:caption>
      <image:title>NumPy ile Tensor Autograd Sıfırdan: Broadcasting-Aware Mini-Tinygrad İnşası</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/pytorch-jax-torch-compile-karsilastirma</loc>
    <lastmod>2026-05-13T13:00:23.906Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/pytorch-jax-torch-compile-karsilastirma"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/pytorch-jax-torch-compile-karsilastirma"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/pytorch-jax-torch-compile-karsilastirma"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>2.2&apos;deki teorik fark → pratik benchmark. Aynı transformer bloğunu PyTorch eager, JAX jit, torch.compile (reduce-overhead, max-autotune) modlarında implement et. Compile time, throughput, memory, debug deneyimi yan yana. 2026&apos;da hangi framework hangi senaryoda?</image:caption>
      <image:title>PyTorch vs JAX vs torch.compile: Eager, Static ve Hybrid&apos;in Pratik Karşılaştırması</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/pytorch-jax-torch-compile-karsilastirma</loc>
    <lastmod>2026-05-13T13:00:23.906Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/pytorch-jax-torch-compile-karsilastirma"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/pytorch-jax-torch-compile-karsilastirma"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/pytorch-jax-torch-compile-karsilastirma"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>2.2&apos;deki teorik fark → pratik benchmark. Aynı transformer bloğunu PyTorch eager, JAX jit, torch.compile (reduce-overhead, max-autotune) modlarında implement et. Compile time, throughput, memory, debug deneyimi yan yana. 2026&apos;da hangi framework hangi senaryoda?</image:caption>
      <image:title>PyTorch vs JAX vs torch.compile: Eager, Static ve Hybrid&apos;in Pratik Karşılaştırması</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/custom-autograd-function-pytorch-internals</loc>
    <lastmod>2026-05-13T13:00:23.992Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/custom-autograd-function-pytorch-internals"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/custom-autograd-function-pytorch-internals"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/custom-autograd-function-pytorch-internals"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>PyTorch autograd&apos;ı extend etmek: torch.autograd.Function subclass&apos;ları, custom forward/backward, ctx ile state saklama, gradcheck doğrulaması, custom CUDA/Triton kernel wrap (preview), FlashAttention block matmul mini-implementasyon, second-order gradients ve gradgradcheck.</image:caption>
      <image:title>Custom autograd.Function ve PyTorch Internals: Kendi Gradient&apos;lerini Yaz</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/custom-autograd-function-pytorch-internals</loc>
    <lastmod>2026-05-13T13:00:23.992Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/custom-autograd-function-pytorch-internals"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/custom-autograd-function-pytorch-internals"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/custom-autograd-function-pytorch-internals"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>PyTorch autograd&apos;ı extend etmek: torch.autograd.Function subclass&apos;ları, custom forward/backward, ctx ile state saklama, gradcheck doğrulaması, custom CUDA/Triton kernel wrap (preview), FlashAttention block matmul mini-implementasyon, second-order gradients ve gradgradcheck.</image:caption>
      <image:title>Custom autograd.Function ve PyTorch Internals: Kendi Gradient&apos;lerini Yaz</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/ysa-70-yillik-yolculuk-perceptron-gpt5</loc>
    <lastmod>2026-05-13T13:00:24.083Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ysa-70-yillik-yolculuk-perceptron-gpt5"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/ysa-70-yillik-yolculuk-perceptron-gpt5"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ysa-70-yillik-yolculuk-perceptron-gpt5"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Derin öğrenmenin tarihi: 1943 McCulloch-Pitts nöronları, 1958 Perceptron, 1986 backprop popülerizasyonu, 1989 LeCun ZIP-code CNN, 1997 LSTM, 2006 Hinton&apos;un DBN paper&apos;ı, 2012 AlexNet, 2017 Transformer, 2022 ChatGPT, 2026 GPT-5. Her milestone&apos;un teknik ve sosyal bağlamı.</image:caption>
      <image:title>Yapay Sinir Ağlarının 70 Yıllık Yolculuğu: McCulloch-Pitts&apos;ten GPT-5&apos;e</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/ysa-70-yillik-yolculuk-perceptron-gpt5</loc>
    <lastmod>2026-05-13T13:00:24.083Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ysa-70-yillik-yolculuk-perceptron-gpt5"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/ysa-70-yillik-yolculuk-perceptron-gpt5"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ysa-70-yillik-yolculuk-perceptron-gpt5"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Derin öğrenmenin tarihi: 1943 McCulloch-Pitts nöronları, 1958 Perceptron, 1986 backprop popülerizasyonu, 1989 LeCun ZIP-code CNN, 1997 LSTM, 2006 Hinton&apos;un DBN paper&apos;ı, 2012 AlexNet, 2017 Transformer, 2022 ChatGPT, 2026 GPT-5. Her milestone&apos;un teknik ve sosyal bağlamı.</image:caption>
      <image:title>Yapay Sinir Ağlarının 70 Yıllık Yolculuğu: McCulloch-Pitts&apos;ten GPT-5&apos;e</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/connectionism-vs-symbolic-llm</loc>
    <lastmod>2026-05-13T13:00:24.172Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/connectionism-vs-symbolic-llm"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/connectionism-vs-symbolic-llm"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/connectionism-vs-symbolic-llm"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Symbolic AI (LISP, expert systems, mantık programlama) ile connectionism (neural networks) arasındaki 60 yıllık felsefi savaş. Bitter Lesson (Sutton 2019), neuro-symbolic hibridler, chain-of-thought ve tool use&apos;un symbolic manipülasyon mu olduğu, LLM reasoning&apos;in geleceği.</image:caption>
      <image:title>Connectionism vs Symbolic: Bitmeyen Tartışmanın 60 Yılı ve LLM&apos;lerin Yeri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/connectionism-vs-symbolic-llm</loc>
    <lastmod>2026-05-13T13:00:24.172Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/connectionism-vs-symbolic-llm"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/connectionism-vs-symbolic-llm"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/connectionism-vs-symbolic-llm"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Symbolic AI (LISP, expert systems, mantık programlama) ile connectionism (neural networks) arasındaki 60 yıllık felsefi savaş. Bitter Lesson (Sutton 2019), neuro-symbolic hibridler, chain-of-thought ve tool use&apos;un symbolic manipülasyon mu olduğu, LLM reasoning&apos;in geleceği.</image:caption>
      <image:title>Connectionism vs Symbolic: Bitmeyen Tartışmanın 60 Yılı ve LLM&apos;lerin Yeri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/vision-big-bang-alexnet-vgg-inception-resnet</loc>
    <lastmod>2026-05-13T13:00:24.261Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vision-big-bang-alexnet-vgg-inception-resnet"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/vision-big-bang-alexnet-vgg-inception-resnet"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vision-big-bang-alexnet-vgg-inception-resnet"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>2012-2017 vision devrimi: AlexNet&apos;in 5 yeniliği, VGG&apos;nin uniformity prensibi, Inception&apos;ın multi-scale yaklaşımı, ResNet&apos;in skip connection devrimi, BatchNorm&apos;un internal covariate shift cevabı. Transformer&apos;a giden mimari mirasın detaylı analizi.</image:caption>
      <image:title>Vision&apos;da Big Bang: AlexNet, VGG, Inception, ResNet, BatchNorm — Modern Mimari Bileşenlerinin Doğuşu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/vision-big-bang-alexnet-vgg-inception-resnet</loc>
    <lastmod>2026-05-13T13:00:24.261Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vision-big-bang-alexnet-vgg-inception-resnet"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/vision-big-bang-alexnet-vgg-inception-resnet"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vision-big-bang-alexnet-vgg-inception-resnet"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>2012-2017 vision devrimi: AlexNet&apos;in 5 yeniliği, VGG&apos;nin uniformity prensibi, Inception&apos;ın multi-scale yaklaşımı, ResNet&apos;in skip connection devrimi, BatchNorm&apos;un internal covariate shift cevabı. Transformer&apos;a giden mimari mirasın detaylı analizi.</image:caption>
      <image:title>Vision&apos;da Big Bang: AlexNet, VGG, Inception, ResNet, BatchNorm — Modern Mimari Bileşenlerinin Doğuşu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/sequence-modelleme-rnn-lstm-attention</loc>
    <lastmod>2026-05-13T13:00:24.352Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/sequence-modelleme-rnn-lstm-attention"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/sequence-modelleme-rnn-lstm-attention"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/sequence-modelleme-rnn-lstm-attention"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>NLP&apos;nin 1990-2017 evrimi: vanilla RNN&apos;in vanishing gradient sorunu, LSTM (Hochreiter 1997) ve GRU çözümü, Seq2Seq (Sutskever 2014), Bahdanau ve Luong attention mekanizmaları, ELMo ile contextual embedding&apos;lerin doğuşu. Bu yolculuk 2017 Transformer&apos;ın zeminini hazırladı.</image:caption>
      <image:title>Sequence Modelleme: RNN, LSTM, GRU&apos;dan Encoder-Decoder ve Attention&apos;a Giden Yol</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/sequence-modelleme-rnn-lstm-attention</loc>
    <lastmod>2026-05-13T13:00:24.352Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/sequence-modelleme-rnn-lstm-attention"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/sequence-modelleme-rnn-lstm-attention"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/sequence-modelleme-rnn-lstm-attention"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>NLP&apos;nin 1990-2017 evrimi: vanilla RNN&apos;in vanishing gradient sorunu, LSTM (Hochreiter 1997) ve GRU çözümü, Seq2Seq (Sutskever 2014), Bahdanau ve Luong attention mekanizmaları, ELMo ile contextual embedding&apos;lerin doğuşu. Bu yolculuk 2017 Transformer&apos;ın zeminini hazırladı.</image:caption>
      <image:title>Sequence Modelleme: RNN, LSTM, GRU&apos;dan Encoder-Decoder ve Attention&apos;a Giden Yol</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/transformer-sonrasi-8-yil-tam-anatomi</loc>
    <lastmod>2026-05-13T13:00:24.446Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/transformer-sonrasi-8-yil-tam-anatomi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/transformer-sonrasi-8-yil-tam-anatomi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/transformer-sonrasi-8-yil-tam-anatomi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Vaswani 2017&apos;den 2026 GPT-5&apos;e transformer&apos;ın 8 yıllık evrim haritası: BERT, GPT serisi, T5, BART, Llama, Claude, DeepSeek, Mistral, Qwen. Pre-training paradigmasının yerleşmesi, scaling laws, RLHF, multimodal yetenek, reasoning model&apos;lar.</image:caption>
      <image:title>Transformer Sonrası 8 Yıl: &apos;Attention Is All You Need&apos;ten GPT-5&apos;e Tam Anatomi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/transformer-sonrasi-8-yil-tam-anatomi</loc>
    <lastmod>2026-05-13T13:00:24.446Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/transformer-sonrasi-8-yil-tam-anatomi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/transformer-sonrasi-8-yil-tam-anatomi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/transformer-sonrasi-8-yil-tam-anatomi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Vaswani 2017&apos;den 2026 GPT-5&apos;e transformer&apos;ın 8 yıllık evrim haritası: BERT, GPT serisi, T5, BART, Llama, Claude, DeepSeek, Mistral, Qwen. Pre-training paradigmasının yerleşmesi, scaling laws, RLHF, multimodal yetenek, reasoning model&apos;lar.</image:caption>
      <image:title>Transformer Sonrası 8 Yıl: &apos;Attention Is All You Need&apos;ten GPT-5&apos;e Tam Anatomi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/llm-conditional-probability-machine</loc>
    <lastmod>2026-05-13T13:00:24.536Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/llm-conditional-probability-machine"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/llm-conditional-probability-machine"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/llm-conditional-probability-machine"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir LLM&apos;in özünde ne olduğunu netleştir: conditional probability dağılımı üretici machine. Autoregressive generation, joint probability&apos;nin chain rule ile decomposition&apos;ı, perplexity ölçümünün gerçek anlamı, neden &apos;hallucination&apos; kaçınılmaz, calibration kavramı, logit ve probability arasındaki ilişki.</image:caption>
      <image:title>LLM Bir Conditional Probability Machine: P(x_t | x_&lt;t) ve Bunun Sonuçları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/llm-conditional-probability-machine</loc>
    <lastmod>2026-05-13T13:00:24.536Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/llm-conditional-probability-machine"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/llm-conditional-probability-machine"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/llm-conditional-probability-machine"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir LLM&apos;in özünde ne olduğunu netleştir: conditional probability dağılımı üretici machine. Autoregressive generation, joint probability&apos;nin chain rule ile decomposition&apos;ı, perplexity ölçümünün gerçek anlamı, neden &apos;hallucination&apos; kaçınılmaz, calibration kavramı, logit ve probability arasındaki ilişki.</image:caption>
      <image:title>LLM Bir Conditional Probability Machine: P(x_t | x_&lt;t) ve Bunun Sonuçları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/tokenization-token-ekonomisi-glitch-tokens</loc>
    <lastmod>2026-05-13T13:00:24.628Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tokenization-token-ekonomisi-glitch-tokens"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tokenization-token-ekonomisi-glitch-tokens"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tokenization-token-ekonomisi-glitch-tokens"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Token sınırlarının tahminleri nasıl şekillendirdiği, Türkçe gibi morfolojik zengin dillerde token ekonomisinin etkisi, SolidGoldMagikarp gibi &apos;glitch tokens&apos;, leading whitespace problemi, prompt engineering&apos;in token-level detayı. Modül 6 (Tokenization Mikro-Cerrahisi) için pratik zemin.</image:caption>
      <image:title>Tokenization Zihinsel Modelin Parçası: Token Ekonomisi, Türkçe Tuzakları ve Glitch Tokens</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tokenization-token-ekonomisi-glitch-tokens</loc>
    <lastmod>2026-05-13T13:00:24.628Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tokenization-token-ekonomisi-glitch-tokens"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tokenization-token-ekonomisi-glitch-tokens"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tokenization-token-ekonomisi-glitch-tokens"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Token sınırlarının tahminleri nasıl şekillendirdiği, Türkçe gibi morfolojik zengin dillerde token ekonomisinin etkisi, SolidGoldMagikarp gibi &apos;glitch tokens&apos;, leading whitespace problemi, prompt engineering&apos;in token-level detayı. Modül 6 (Tokenization Mikro-Cerrahisi) için pratik zemin.</image:caption>
      <image:title>Tokenization Zihinsel Modelin Parçası: Token Ekonomisi, Türkçe Tuzakları ve Glitch Tokens</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/sampling-sanati-temperature-top-p-min-p-dry</loc>
    <lastmod>2026-05-13T13:00:24.719Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/sampling-sanati-temperature-top-p-min-p-dry"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/sampling-sanati-temperature-top-p-min-p-dry"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/sampling-sanati-temperature-top-p-min-p-dry"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Production-level sampling stratejileri: temperature/top-k/top-p/min-p/typical-p/tail-free/DRY repetition penalty, beam search ve diverse beam, contrastive decoding, speculative sampling, reasoning model&apos;larda sampling, structured output ile sampling, multi-sample self-consistency.</image:caption>
      <image:title>Sampling Sanatı Derinlemesine: Greedy, Beam, Top-K, Top-P, Min-P, DRY, Tail-Free — Hepsi Production&apos;da</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/sampling-sanati-temperature-top-p-min-p-dry</loc>
    <lastmod>2026-05-13T13:00:24.719Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/sampling-sanati-temperature-top-p-min-p-dry"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/sampling-sanati-temperature-top-p-min-p-dry"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/sampling-sanati-temperature-top-p-min-p-dry"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Production-level sampling stratejileri: temperature/top-k/top-p/min-p/typical-p/tail-free/DRY repetition penalty, beam search ve diverse beam, contrastive decoding, speculative sampling, reasoning model&apos;larda sampling, structured output ile sampling, multi-sample self-consistency.</image:caption>
      <image:title>Sampling Sanatı Derinlemesine: Greedy, Beam, Top-K, Top-P, Min-P, DRY, Tail-Free — Hepsi Production&apos;da</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/logit-gozlemciligi-logprobs-production</loc>
    <lastmod>2026-05-13T13:00:24.812Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/logit-gozlemciligi-logprobs-production"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/logit-gozlemciligi-logprobs-production"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/logit-gozlemciligi-logprobs-production"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>logprobs API&apos;sının production-grade kullanımı: confidence-based filtering, hallucination detection, prompt diagnostics, model probing, MCQ scoring, semantic confidence, anomaly detection. logits/probability/log-probability dönüşümleri, token-level entropy, ekstraksiyon teknikleri.</image:caption>
      <image:title>Logit Gözlemciliği: logprobs ile Modelin Zihnini Okuma — Production Diagnostics</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/logit-gozlemciligi-logprobs-production</loc>
    <lastmod>2026-05-13T13:00:24.812Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/logit-gozlemciligi-logprobs-production"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/logit-gozlemciligi-logprobs-production"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/logit-gozlemciligi-logprobs-production"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>logprobs API&apos;sının production-grade kullanımı: confidence-based filtering, hallucination detection, prompt diagnostics, model probing, MCQ scoring, semantic confidence, anomaly detection. logits/probability/log-probability dönüşümleri, token-level entropy, ekstraksiyon teknikleri.</image:caption>
      <image:title>Logit Gözlemciliği: logprobs ile Modelin Zihnini Okuma — Production Diagnostics</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/in-context-learning-matematik-induction-heads</loc>
    <lastmod>2026-05-13T13:00:24.900Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/in-context-learning-matematik-induction-heads"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/in-context-learning-matematik-induction-heads"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/in-context-learning-matematik-induction-heads"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GPT-3&apos;ün few-shot learning yeteneğinin matematiksel açıklamaları: implicit Bayesian inference (Xie 2022), induction heads mechanism (Olsson 2022), task identification ve learning algorithm emergence. Prompt&apos;a örnek vermek niye çalışıyor, niye yeterince büyük modellerde, niye OOD&apos;da çuvallıyor.</image:caption>
      <image:title>In-Context Learning&apos;in Matematiği: Implicit Bayesian Inference ve Induction Heads</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/in-context-learning-matematik-induction-heads</loc>
    <lastmod>2026-05-13T13:00:24.900Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/in-context-learning-matematik-induction-heads"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/in-context-learning-matematik-induction-heads"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/in-context-learning-matematik-induction-heads"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GPT-3&apos;ün few-shot learning yeteneğinin matematiksel açıklamaları: implicit Bayesian inference (Xie 2022), induction heads mechanism (Olsson 2022), task identification ve learning algorithm emergence. Prompt&apos;a örnek vermek niye çalışıyor, niye yeterince büyük modellerde, niye OOD&apos;da çuvallıyor.</image:caption>
      <image:title>In-Context Learning&apos;in Matematiği: Implicit Bayesian Inference ve Induction Heads</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/scaling-laws-kaplan-chinchilla-post-chinchilla</loc>
    <lastmod>2026-05-13T13:00:25.002Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/scaling-laws-kaplan-chinchilla-post-chinchilla"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/scaling-laws-kaplan-chinchilla-post-chinchilla"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/scaling-laws-kaplan-chinchilla-post-chinchilla"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM eğitiminin matematiksel temellerinin tam analizi: Kaplan 2020 power laws, Chinchilla 2022 compute-optimal teoremi, post-Chinchilla over-training (Llama 3 yaklaşımı), inference-aware scaling (Sardana 2023), μP hyperparameter transfer, FLOP hesaplama, MFU optimization.</image:caption>
      <image:title>Scaling Laws Sezgisi: Kaplan, Chinchilla, Post-Chinchilla — LLM Eğitiminin Matematiksel Planlaması</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/scaling-laws-kaplan-chinchilla-post-chinchilla</loc>
    <lastmod>2026-05-13T13:00:25.002Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/scaling-laws-kaplan-chinchilla-post-chinchilla"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/scaling-laws-kaplan-chinchilla-post-chinchilla"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/scaling-laws-kaplan-chinchilla-post-chinchilla"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM eğitiminin matematiksel temellerinin tam analizi: Kaplan 2020 power laws, Chinchilla 2022 compute-optimal teoremi, post-Chinchilla over-training (Llama 3 yaklaşımı), inference-aware scaling (Sardana 2023), μP hyperparameter transfer, FLOP hesaplama, MFU optimization.</image:caption>
      <image:title>Scaling Laws Sezgisi: Kaplan, Chinchilla, Post-Chinchilla — LLM Eğitiminin Matematiksel Planlaması</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/emergent-capabilities-mirage-gercek</loc>
    <lastmod>2026-05-13T13:00:25.089Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/emergent-capabilities-mirage-gercek"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/emergent-capabilities-mirage-gercek"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/emergent-capabilities-mirage-gercek"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GPT-3 paper&apos;ının &apos;emergent abilities&apos; iddiası, Wei 2022&apos;nin systematic çalışması, Schaeffer 2023&apos;ün &apos;Are Emergent Abilities a Mirage?&apos; meydan okuması. Threshold effects, metric design, smooth vs discontinuous capabilities. Hangi yetenek gerçekten emergent, hangisi ölçüm artefaktı?</image:caption>
      <image:title>Emergent Capabilities: &apos;Sudden&apos; Yetenekler Gerçek mi, Ölçüm Artefaktı mı?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/emergent-capabilities-mirage-gercek</loc>
    <lastmod>2026-05-13T13:00:25.089Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/emergent-capabilities-mirage-gercek"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/emergent-capabilities-mirage-gercek"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/emergent-capabilities-mirage-gercek"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GPT-3 paper&apos;ının &apos;emergent abilities&apos; iddiası, Wei 2022&apos;nin systematic çalışması, Schaeffer 2023&apos;ün &apos;Are Emergent Abilities a Mirage?&apos; meydan okuması. Threshold effects, metric design, smooth vs discontinuous capabilities. Hangi yetenek gerçekten emergent, hangisi ölçüm artefaktı?</image:caption>
      <image:title>Emergent Capabilities: &apos;Sudden&apos; Yetenekler Gerçek mi, Ölçüm Artefaktı mı?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/memorization-generalization-paraphrase</loc>
    <lastmod>2026-05-13T13:00:25.176Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/memorization-generalization-paraphrase"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/memorization-generalization-paraphrase"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/memorization-generalization-paraphrase"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM training corpus&apos;u &apos;ezberliyor&apos; mu yoksa &apos;genelleyerek öğreniyor&apos; mu? Exact match tests, paraphrase resistance, contamination detection, membership inference. Eval&apos;de memorization detection, training data extraction risks, privacy implications.</image:caption>
      <image:title>Memorization vs Generalization: Paraphrase Testleri ve LLM&apos;in Gerçek Anlayışı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/memorization-generalization-paraphrase</loc>
    <lastmod>2026-05-13T13:00:25.176Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/memorization-generalization-paraphrase"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/memorization-generalization-paraphrase"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/memorization-generalization-paraphrase"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM training corpus&apos;u &apos;ezberliyor&apos; mu yoksa &apos;genelleyerek öğreniyor&apos; mu? Exact match tests, paraphrase resistance, contamination detection, membership inference. Eval&apos;de memorization detection, training data extraction risks, privacy implications.</image:caption>
      <image:title>Memorization vs Generalization: Paraphrase Testleri ve LLM&apos;in Gerçek Anlayışı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/torch-compile-torch-fx-graph-capture</loc>
    <lastmod>2026-05-13T13:00:25.262Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/torch-compile-torch-fx-graph-capture"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/torch-compile-torch-fx-graph-capture"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/torch-compile-torch-fx-graph-capture"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>PyTorch 2.0+&apos;ın game-changer feature&apos;ı torch.compile derinlemesine: TorchDynamo + TorchInductor + Triton akışı, FX graph manipulation, compile modes (default/reduce-overhead/max-autotune), graph breaks debugging, dynamic shapes, production trade-off&apos;lar. Modül 2.5&apos;in production extension&apos;ı.</image:caption>
      <image:title>torch.compile ve torch.fx: Graph Capture, JIT Compilation ve Production Optimization</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/torch-compile-torch-fx-graph-capture</loc>
    <lastmod>2026-05-13T13:00:25.262Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/torch-compile-torch-fx-graph-capture"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/torch-compile-torch-fx-graph-capture"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/torch-compile-torch-fx-graph-capture"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>PyTorch 2.0+&apos;ın game-changer feature&apos;ı torch.compile derinlemesine: TorchDynamo + TorchInductor + Triton akışı, FX graph manipulation, compile modes (default/reduce-overhead/max-autotune), graph breaks debugging, dynamic shapes, production trade-off&apos;lar. Modül 2.5&apos;in production extension&apos;ı.</image:caption>
      <image:title>torch.compile ve torch.fx: Graph Capture, JIT Compilation ve Production Optimization</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/mixed-precision-bf16-fp16-fp8-autocast</loc>
    <lastmod>2026-05-13T13:00:25.352Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/mixed-precision-bf16-fp16-fp8-autocast"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/mixed-precision-bf16-fp16-fp8-autocast"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/mixed-precision-bf16-fp16-fp8-autocast"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 1.9&apos;da numerik stabilite temellerini gördük. Bu derste production mixed precision: autocast region tasarımı, GradScaler dinamikleri, FP8 H100/B200 native training (DeepSeek-V3 yöntemi), gradient norm monitoring, loss spike investigation, BF16 vs FP16 production karar matrisi.</image:caption>
      <image:title>Mixed Precision Training Derinlemesine: BF16, FP16, FP8, autocast, GradScaler — Production Patterns</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/mixed-precision-bf16-fp16-fp8-autocast</loc>
    <lastmod>2026-05-13T13:00:25.352Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/mixed-precision-bf16-fp16-fp8-autocast"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/mixed-precision-bf16-fp16-fp8-autocast"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/mixed-precision-bf16-fp16-fp8-autocast"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 1.9&apos;da numerik stabilite temellerini gördük. Bu derste production mixed precision: autocast region tasarımı, GradScaler dinamikleri, FP8 H100/B200 native training (DeepSeek-V3 yöntemi), gradient norm monitoring, loss spike investigation, BF16 vs FP16 production karar matrisi.</image:caption>
      <image:title>Mixed Precision Training Derinlemesine: BF16, FP16, FP8, autocast, GradScaler — Production Patterns</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/memory-profiling-torch-profiler-nsight-oom</loc>
    <lastmod>2026-05-13T13:00:25.440Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/memory-profiling-torch-profiler-nsight-oom"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/memory-profiling-torch-profiler-nsight-oom"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/memory-profiling-torch-profiler-nsight-oom"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517077304055-6e89abbf09b0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GPU memory&apos;sinin gizli yaşamı: aktivasyon vs gradient vs optimizer state breakdown, torch.profiler ile memory snapshot, Nsight Systems timeline analizi, OOM root cause analysis, activation checkpointing, gradient accumulation, fragmentation çözümleri.</image:caption>
      <image:title>Memory Profiling: torch.profiler, Nsight Systems, OOM Debugging — Production GPU Memory Yönetimi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/memory-profiling-torch-profiler-nsight-oom</loc>
    <lastmod>2026-05-13T13:00:25.440Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/memory-profiling-torch-profiler-nsight-oom"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/memory-profiling-torch-profiler-nsight-oom"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/memory-profiling-torch-profiler-nsight-oom"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517077304055-6e89abbf09b0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GPU memory&apos;sinin gizli yaşamı: aktivasyon vs gradient vs optimizer state breakdown, torch.profiler ile memory snapshot, Nsight Systems timeline analizi, OOM root cause analysis, activation checkpointing, gradient accumulation, fragmentation çözümleri.</image:caption>
      <image:title>Memory Profiling: torch.profiler, Nsight Systems, OOM Debugging — Production GPU Memory Yönetimi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/cuda-streams-events-nccl-temelleri</loc>
    <lastmod>2026-05-13T13:00:25.529Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/cuda-streams-events-nccl-temelleri"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/cuda-streams-events-nccl-temelleri"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/cuda-streams-events-nccl-temelleri"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1531297484001-80022131f5a1?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GPU&apos;da concurrency: streams ile parallel kernel execution, events ile fine-grained synchronization, NCCL collective operations (allreduce, broadcast, all-gather, reduce-scatter). Distributed training&apos;in altyapı katmanı. Modül 17 (Distributed Training) için ön hazırlık.</image:caption>
      <image:title>CUDA Streams, Events ve NCCL Temelleri: Multi-GPU Communication&apos;ın Alt Katmanı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/cuda-streams-events-nccl-temelleri</loc>
    <lastmod>2026-05-13T13:00:25.529Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/cuda-streams-events-nccl-temelleri"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/cuda-streams-events-nccl-temelleri"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/cuda-streams-events-nccl-temelleri"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1531297484001-80022131f5a1?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GPU&apos;da concurrency: streams ile parallel kernel execution, events ile fine-grained synchronization, NCCL collective operations (allreduce, broadcast, all-gather, reduce-scatter). Distributed training&apos;in altyapı katmanı. Modül 17 (Distributed Training) için ön hazırlık.</image:caption>
      <image:title>CUDA Streams, Events ve NCCL Temelleri: Multi-GPU Communication&apos;ın Alt Katmanı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/triton-custom-gpu-kernels-flashattention</loc>
    <lastmod>2026-05-13T13:00:25.618Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/triton-custom-gpu-kernels-flashattention"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/triton-custom-gpu-kernels-flashattention"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/triton-custom-gpu-kernels-flashattention"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Triton&apos;un Python syntax ile GPU programming sırrı: programming model (program_id, block_size, autotune), softmax kernel sıfırdan, matmul tiling, FlashAttention&apos;ın block-wise mini implementasyonu, performans tuning. Modül 37 (CUDA/Triton derin dalış) için pratik temel.</image:caption>
      <image:title>Triton ile Custom GPU Kernels: Softmax, Matmul, FlashAttention Mini Sıfırdan</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/triton-custom-gpu-kernels-flashattention</loc>
    <lastmod>2026-05-13T13:00:25.618Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/triton-custom-gpu-kernels-flashattention"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/triton-custom-gpu-kernels-flashattention"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/triton-custom-gpu-kernels-flashattention"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Triton&apos;un Python syntax ile GPU programming sırrı: programming model (program_id, block_size, autotune), softmax kernel sıfırdan, matmul tiling, FlashAttention&apos;ın block-wise mini implementasyonu, performans tuning. Modül 37 (CUDA/Triton derin dalış) için pratik temel.</image:caption>
      <image:title>Triton ile Custom GPU Kernels: Softmax, Matmul, FlashAttention Mini Sıfırdan</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/torch-distributed-ddp-fsdp-zero-stages</loc>
    <lastmod>2026-05-13T13:00:25.708Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/torch-distributed-ddp-fsdp-zero-stages"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/torch-distributed-ddp-fsdp-zero-stages"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/torch-distributed-ddp-fsdp-zero-stages"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>5.4&apos;te NCCL temellerini gördük. Şimdi production distributed training stack: DDP gradient bucketing + overlap, FSDP shard strategies (FULL_SHARD, SHARD_GRAD_OP, HYBRID_SHARD), DeepSpeed ZeRO Stage 1/2/3 karşılaştırma, hybrid 3D parallelism. Modül 17 için son köprü.</image:caption>
      <image:title>torch.distributed Derinleştirilmiş: DDP, FSDP, ZeRO Stages — Production Distributed Training</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/torch-distributed-ddp-fsdp-zero-stages</loc>
    <lastmod>2026-05-13T13:00:25.708Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/torch-distributed-ddp-fsdp-zero-stages"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/torch-distributed-ddp-fsdp-zero-stages"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/torch-distributed-ddp-fsdp-zero-stages"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>5.4&apos;te NCCL temellerini gördük. Şimdi production distributed training stack: DDP gradient bucketing + overlap, FSDP shard strategies (FULL_SHARD, SHARD_GRAD_OP, HYBRID_SHARD), DeepSpeed ZeRO Stage 1/2/3 karşılaştırma, hybrid 3D parallelism. Modül 17 için son köprü.</image:caption>
      <image:title>torch.distributed Derinleştirilmiş: DDP, FSDP, ZeRO Stages — Production Distributed Training</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/debug-arsenal-hooks-anomaly-benchmark</loc>
    <lastmod>2026-05-13T13:00:25.796Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/debug-arsenal-hooks-anomaly-benchmark"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/debug-arsenal-hooks-anomaly-benchmark"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/debug-arsenal-hooks-anomaly-benchmark"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Production PyTorch&apos;ta iş bozulduğunda toolkit: forward/backward hooks, anomaly detection mode, deterministic training, torch.utils.benchmark precise timing, repro pattern&apos;leri, NaN avı systematik, gradient inspection, model debugging stratejileri.</image:caption>
      <image:title>Debug Arsenal: register_hook, Anomaly Mode, torch.utils.benchmark — Production Debugging Toolkit</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/debug-arsenal-hooks-anomaly-benchmark</loc>
    <lastmod>2026-05-13T13:00:25.796Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/debug-arsenal-hooks-anomaly-benchmark"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/debug-arsenal-hooks-anomaly-benchmark"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/debug-arsenal-hooks-anomaly-benchmark"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Production PyTorch&apos;ta iş bozulduğunda toolkit: forward/backward hooks, anomaly detection mode, deterministic training, torch.utils.benchmark precise timing, repro pattern&apos;leri, NaN avı systematik, gradient inspection, model debugging stratejileri.</image:caption>
      <image:title>Debug Arsenal: register_hook, Anomaly Mode, torch.utils.benchmark — Production Debugging Toolkit</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/production-engineering-cicd-versioning-deployment</loc>
    <lastmod>2026-05-13T13:00:25.894Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/production-engineering-cicd-versioning-deployment"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/production-engineering-cicd-versioning-deployment"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/production-engineering-cicd-versioning-deployment"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>PyTorch mühendisliğinin son dersi — production workflow patterns: ML CI/CD pipelines, eval harness CI&apos;a integration, model + prompt + data versioning (DVC, MLflow, HF Hub), canary deployment, A/B testing, rollback strategies, drift monitoring, KVKK uyumlu deploy. Part I&apos;in kapanışı.</image:caption>
      <image:title>Production Engineering: Reproducibility, CI/CD for ML, Versioning ve Deployment Patterns</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/production-engineering-cicd-versioning-deployment</loc>
    <lastmod>2026-05-13T13:00:25.894Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/production-engineering-cicd-versioning-deployment"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/production-engineering-cicd-versioning-deployment"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/production-engineering-cicd-versioning-deployment"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>PyTorch mühendisliğinin son dersi — production workflow patterns: ML CI/CD pipelines, eval harness CI&apos;a integration, model + prompt + data versioning (DVC, MLflow, HF Hub), canary deployment, A/B testing, rollback strategies, drift monitoring, KVKK uyumlu deploy. Part I&apos;in kapanışı.</image:caption>
      <image:title>Production Engineering: Reproducibility, CI/CD for ML, Versioning ve Deployment Patterns</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/tokenization-karakter-sozcuk-subword-karar</loc>
    <lastmod>2026-05-13T13:00:25.982Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tokenization-karakter-sozcuk-subword-karar"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tokenization-karakter-sozcuk-subword-karar"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tokenization-karakter-sozcuk-subword-karar"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tokenization tasarım uzayı: karakter-level (UTF-8, byte), sözcük-level (whitespace, morfoloji), subword (BPE, WordPiece, Unigram). Her seçimin matematiksel ve pragmatik trade-off&apos;ları, OOV problemi, vocabulary size karar matrisi, multilingual zorlukları, Türkçe karakteristikleri.</image:caption>
      <image:title>Karakter, Sözcük, Subword: Tokenization Tasarım Baskıları ve Karar Matrisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tokenization-karakter-sozcuk-subword-karar</loc>
    <lastmod>2026-05-13T13:00:25.982Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tokenization-karakter-sozcuk-subword-karar"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tokenization-karakter-sozcuk-subword-karar"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tokenization-karakter-sozcuk-subword-karar"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tokenization tasarım uzayı: karakter-level (UTF-8, byte), sözcük-level (whitespace, morfoloji), subword (BPE, WordPiece, Unigram). Her seçimin matematiksel ve pragmatik trade-off&apos;ları, OOV problemi, vocabulary size karar matrisi, multilingual zorlukları, Türkçe karakteristikleri.</image:caption>
      <image:title>Karakter, Sözcük, Subword: Tokenization Tasarım Baskıları ve Karar Matrisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/bpe-algoritma-sennrich-pseudocode-complexity</loc>
    <lastmod>2026-05-13T13:00:26.070Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/bpe-algoritma-sennrich-pseudocode-complexity"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/bpe-algoritma-sennrich-pseudocode-complexity"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/bpe-algoritma-sennrich-pseudocode-complexity"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>BPE&apos;nin matematik anatomi. Sennrich 2016 paper&apos;ı satır satır: pre-tokenization, byte-pair merge counting, greedy merge selection, vocabulary inşası, encoding logic, complexity analysis (O(N·V)), edge cases (Unicode, whitespace, special tokens). Modül 6.3&apos;te implement öncesi tam kavrama.</image:caption>
      <image:title>BPE Algoritması: Sennrich 2016 Satır Satır — Pseudocode, Complexity, Edge Cases</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/bpe-algoritma-sennrich-pseudocode-complexity</loc>
    <lastmod>2026-05-13T13:00:26.070Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/bpe-algoritma-sennrich-pseudocode-complexity"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/bpe-algoritma-sennrich-pseudocode-complexity"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/bpe-algoritma-sennrich-pseudocode-complexity"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>BPE&apos;nin matematik anatomi. Sennrich 2016 paper&apos;ı satır satır: pre-tokenization, byte-pair merge counting, greedy merge selection, vocabulary inşası, encoding logic, complexity analysis (O(N·V)), edge cases (Unicode, whitespace, special tokens). Modül 6.3&apos;te implement öncesi tam kavrama.</image:caption>
      <image:title>BPE Algoritması: Sennrich 2016 Satır Satır — Pseudocode, Complexity, Edge Cases</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/bpe-sifirdan-200-satir-turkce-corpus</loc>
    <lastmod>2026-05-13T13:00:26.162Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/bpe-sifirdan-200-satir-turkce-corpus"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/bpe-sifirdan-200-satir-turkce-corpus"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/bpe-sifirdan-200-satir-turkce-corpus"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Karpathy minbpe stil sıfırdan implementation: pure Python BPE training (Sennrich algorithm), encoding/decoding, regex pre-tokenization, byte-level extension, Türkçe corpus üzerinde train + Trendyol-LLM ile karşılaştırma. Modern LLM tokenizer&apos;larını pratik anlama.</image:caption>
      <image:title>BPE&apos;yi 200 Satırda Sıfırdan Yaz: Training + Encoding + Decoding + Türkçe Corpus</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/bpe-sifirdan-200-satir-turkce-corpus</loc>
    <lastmod>2026-05-13T13:00:26.162Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/bpe-sifirdan-200-satir-turkce-corpus"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/bpe-sifirdan-200-satir-turkce-corpus"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/bpe-sifirdan-200-satir-turkce-corpus"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Karpathy minbpe stil sıfırdan implementation: pure Python BPE training (Sennrich algorithm), encoding/decoding, regex pre-tokenization, byte-level extension, Türkçe corpus üzerinde train + Trendyol-LLM ile karşılaştırma. Modern LLM tokenizer&apos;larını pratik anlama.</image:caption>
      <image:title>BPE&apos;yi 200 Satırda Sıfırdan Yaz: Training + Encoding + Decoding + Türkçe Corpus</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/wordpiece-bert-likelihood-merges</loc>
    <lastmod>2026-05-13T13:00:26.254Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/wordpiece-bert-likelihood-merges"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/wordpiece-bert-likelihood-merges"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/wordpiece-bert-likelihood-merges"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>WordPiece algoritması: Schuster &amp; Nakajima 2012&apos;den BERT 2018&apos;e yolculuk. Frequency yerine likelihood-based merge skoru, ##suffix prefix konvansiyonu, [UNK]/[CLS]/[SEP] special tokens, BPE&apos;den sessiz ama kritik farklılıklar. HuggingFace Tokenizers ile pratik training, BERT-base-Turkish-cased örneği, vocab tasarımı.</image:caption>
      <image:title>WordPiece (BERT): Likelihood-Based Merges ve BPE&apos;den Sessiz Farklılıklar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/wordpiece-bert-likelihood-merges</loc>
    <lastmod>2026-05-13T13:00:26.254Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/wordpiece-bert-likelihood-merges"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/wordpiece-bert-likelihood-merges"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/wordpiece-bert-likelihood-merges"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>WordPiece algoritması: Schuster &amp; Nakajima 2012&apos;den BERT 2018&apos;e yolculuk. Frequency yerine likelihood-based merge skoru, ##suffix prefix konvansiyonu, [UNK]/[CLS]/[SEP] special tokens, BPE&apos;den sessiz ama kritik farklılıklar. HuggingFace Tokenizers ile pratik training, BERT-base-Turkish-cased örneği, vocab tasarımı.</image:caption>
      <image:title>WordPiece (BERT): Likelihood-Based Merges ve BPE&apos;den Sessiz Farklılıklar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/sentencepiece-unigram-lm-kudo</loc>
    <lastmod>2026-05-13T13:00:26.342Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/sentencepiece-unigram-lm-kudo"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/sentencepiece-unigram-lm-kudo"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/sentencepiece-unigram-lm-kudo"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>SentencePiece framework + Unigram language model algoritması. Kudo 2018&apos;in olasılıksal yaklaşımı: büyük vocab&apos;tan başla, EM ile budama. Viterbi forward encoding, subword regularization, ▁ whitespace-as-character. Llama, T5, Mistral&apos;in tercihi. Türkçe ve multilingual avantajları.</image:caption>
      <image:title>SentencePiece + Unigram LM (Kudo 2018): Olasılıksal Tokenizasyon ve Subword Regularization</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/sentencepiece-unigram-lm-kudo</loc>
    <lastmod>2026-05-13T13:00:26.342Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/sentencepiece-unigram-lm-kudo"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/sentencepiece-unigram-lm-kudo"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/sentencepiece-unigram-lm-kudo"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>SentencePiece framework + Unigram language model algoritması. Kudo 2018&apos;in olasılıksal yaklaşımı: büyük vocab&apos;tan başla, EM ile budama. Viterbi forward encoding, subword regularization, ▁ whitespace-as-character. Llama, T5, Mistral&apos;in tercihi. Türkçe ve multilingual avantajları.</image:caption>
      <image:title>SentencePiece + Unigram LM (Kudo 2018): Olasılıksal Tokenizasyon ve Subword Regularization</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/gpt-byte-level-bpe-tiktoken-regex</loc>
    <lastmod>2026-05-13T13:00:26.435Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/gpt-byte-level-bpe-tiktoken-regex"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/gpt-byte-level-bpe-tiktoken-regex"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/gpt-byte-level-bpe-tiktoken-regex"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GPT-2 byte-level BPE&apos;nin doğuşu (Radford 2019), regex pre-tokenizer&apos;ın sırrı, GPT-3.5 cl100k, GPT-4o o200k, Llama-3&apos;ün tiktoken&apos;a geri dönüşü. tiktoken Rust performansı, prompt engineering için token counting, Türkçe maliyet ekonomisi, encoding rejimlerinin kıyaslaması.</image:caption>
      <image:title>GPT-2/GPT-4 Byte-Level BPE + tiktoken Regex: Modern Standardın Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/gpt-byte-level-bpe-tiktoken-regex</loc>
    <lastmod>2026-05-13T13:00:26.435Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/gpt-byte-level-bpe-tiktoken-regex"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/gpt-byte-level-bpe-tiktoken-regex"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/gpt-byte-level-bpe-tiktoken-regex"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GPT-2 byte-level BPE&apos;nin doğuşu (Radford 2019), regex pre-tokenizer&apos;ın sırrı, GPT-3.5 cl100k, GPT-4o o200k, Llama-3&apos;ün tiktoken&apos;a geri dönüşü. tiktoken Rust performansı, prompt engineering için token counting, Türkçe maliyet ekonomisi, encoding rejimlerinin kıyaslaması.</image:caption>
      <image:title>GPT-2/GPT-4 Byte-Level BPE + tiktoken Regex: Modern Standardın Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/special-tokens-chatml-chat-templates</loc>
    <lastmod>2026-05-13T13:00:26.556Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/special-tokens-chatml-chat-templates"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/special-tokens-chatml-chat-templates"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/special-tokens-chatml-chat-templates"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Chat formatlarının doğuşu (ChatGPT Mart 2022), ChatML resmi spec, &lt;|im_start|&gt;/&lt;|im_end|&gt;/&lt;|im_sep|&gt; token anatomisi, Llama-3 Instruct + Mistral [INST] + Claude Messages API + Gemini formatları, HuggingFace chat_template Jinja2, system prompt placement, tool use tokenları, prompt injection güvenliği, multi-turn token ekonomisi, Türkçe chat pratiği.</image:caption>
      <image:title>Special Tokens + ChatML + Chat Templates: Konuşan LLM&apos;in Tokenization Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/special-tokens-chatml-chat-templates</loc>
    <lastmod>2026-05-13T13:00:26.556Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/special-tokens-chatml-chat-templates"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/special-tokens-chatml-chat-templates"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/special-tokens-chatml-chat-templates"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Chat formatlarının doğuşu (ChatGPT Mart 2022), ChatML resmi spec, &lt;|im_start|&gt;/&lt;|im_end|&gt;/&lt;|im_sep|&gt; token anatomisi, Llama-3 Instruct + Mistral [INST] + Claude Messages API + Gemini formatları, HuggingFace chat_template Jinja2, system prompt placement, tool use tokenları, prompt injection güvenliği, multi-turn token ekonomisi, Türkçe chat pratiği.</image:caption>
      <image:title>Special Tokens + ChatML + Chat Templates: Konuşan LLM&apos;in Tokenization Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/huggingface-tokenizers-rust-production-pipeline</loc>
    <lastmod>2026-05-13T13:00:26.648Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/huggingface-tokenizers-rust-production-pipeline"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/huggingface-tokenizers-rust-production-pipeline"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/huggingface-tokenizers-rust-production-pipeline"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1542903660-eedba2cda473?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>HuggingFace tokenizers crate&apos;inin Rust mimarisi, 6 katmanlı pipeline (Normalizer → PreTokenizer → Model → PostProcessor → Decoder → Trainer), tokenizer.json format anatomisi, Türkçe production-grade end-to-end training, Rust internals (parallel processing, SIMD, ahash, mmap), tiktoken/SentencePiece conversion, threading + caching + FFI overhead, benchmarklar.</image:caption>
      <image:title>HuggingFace Tokenizers Rust + Production Pipeline: Üretim-Kalite Tokenizer&apos;ı Sıfırdan Eğitmek</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/huggingface-tokenizers-rust-production-pipeline</loc>
    <lastmod>2026-05-13T13:00:26.648Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/huggingface-tokenizers-rust-production-pipeline"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/huggingface-tokenizers-rust-production-pipeline"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/huggingface-tokenizers-rust-production-pipeline"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1542903660-eedba2cda473?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>HuggingFace tokenizers crate&apos;inin Rust mimarisi, 6 katmanlı pipeline (Normalizer → PreTokenizer → Model → PostProcessor → Decoder → Trainer), tokenizer.json format anatomisi, Türkçe production-grade end-to-end training, Rust internals (parallel processing, SIMD, ahash, mmap), tiktoken/SentencePiece conversion, threading + caching + FFI overhead, benchmarklar.</image:caption>
      <image:title>HuggingFace Tokenizers Rust + Production Pipeline: Üretim-Kalite Tokenizer&apos;ı Sıfırdan Eğitmek</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/tokenizer-evaluation-fertility-compression-downstream</loc>
    <lastmod>2026-05-13T13:00:26.742Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tokenizer-evaluation-fertility-compression-downstream"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tokenizer-evaluation-fertility-compression-downstream"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tokenizer-evaluation-fertility-compression-downstream"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tokenizer kalitesini ölçen tüm metriklerin derin anatomisi: fertility (token/word), compression ratio (bytes/token), OOV rate, bits-per-character (BPC), perplexity&apos;ye etki, cross-lingual fertility, downstream task impact, vocab coverage, A/B testing protokolleri, Türkçe-spesifik metrikler, maliyet &apos;vergi&apos; analizi, capstone evaluation framework.</image:caption>
      <image:title>Tokenizer Evaluation: Fertility, Compression Ratio, Downstream Impact ve Bilgi Teorik Ölçümler</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tokenizer-evaluation-fertility-compression-downstream</loc>
    <lastmod>2026-05-13T13:00:26.742Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tokenizer-evaluation-fertility-compression-downstream"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tokenizer-evaluation-fertility-compression-downstream"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tokenizer-evaluation-fertility-compression-downstream"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tokenizer kalitesini ölçen tüm metriklerin derin anatomisi: fertility (token/word), compression ratio (bytes/token), OOV rate, bits-per-character (BPC), perplexity&apos;ye etki, cross-lingual fertility, downstream task impact, vocab coverage, A/B testing protokolleri, Türkçe-spesifik metrikler, maliyet &apos;vergi&apos; analizi, capstone evaluation framework.</image:caption>
      <image:title>Tokenizer Evaluation: Fertility, Compression Ratio, Downstream Impact ve Bilgi Teorik Ölçümler</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turktokenizer-tr-huggingface-hub</loc>
    <lastmod>2026-05-13T13:00:26.836Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turktokenizer-tr-huggingface-hub"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turktokenizer-tr-huggingface-hub"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turktokenizer-tr-huggingface-hub"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 6&apos;nın eseri: TurkTokenizer-tr 32K vocab Türkçe BPE&apos;i sıfırdan eğit, 6.9&apos;un evaluation framework&apos;ü ile değerlendir, model card yaz, license seç, HuggingFace Hub&apos;a publish et. Corpus curation (Wikipedia + OSCAR + news + literature + code), cleaning pipeline, chat template, production integration, maintenance roadmap. Modül 6.1-6.9&apos;un sentezi, gerçek dünya artefakt.</image:caption>
      <image:title>Capstone TurkTokenizer-tr: Türkçe Production-Grade Tokenizer Eğit, Değerlendir ve HuggingFace Hub&apos;a Yayınla</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turktokenizer-tr-huggingface-hub</loc>
    <lastmod>2026-05-13T13:00:26.836Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turktokenizer-tr-huggingface-hub"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turktokenizer-tr-huggingface-hub"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turktokenizer-tr-huggingface-hub"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 6&apos;nın eseri: TurkTokenizer-tr 32K vocab Türkçe BPE&apos;i sıfırdan eğit, 6.9&apos;un evaluation framework&apos;ü ile değerlendir, model card yaz, license seç, HuggingFace Hub&apos;a publish et. Corpus curation (Wikipedia + OSCAR + news + literature + code), cleaning pipeline, chat template, production integration, maintenance roadmap. Modül 6.1-6.9&apos;un sentezi, gerçek dünya artefakt.</image:caption>
      <image:title>Capstone TurkTokenizer-tr: Türkçe Production-Grade Tokenizer Eğit, Değerlendir ve HuggingFace Hub&apos;a Yayınla</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/embedding-nedir-token-id-vektor-koprusu</loc>
    <lastmod>2026-05-13T13:00:26.928Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/embedding-nedir-token-id-vektor-koprusu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/embedding-nedir-token-id-vektor-koprusu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/embedding-nedir-token-id-vektor-koprusu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Embedding&apos;in matematiksel anatomisi: integer token ID&apos;sini d-dimensional dense vector&apos;e mapping. Vocab × d_model matrisi. One-hot encoding&apos;in dejenere durumu. Niye semantic vector space çalışıyor (distributional hypothesis, Firth 1957). Embedding&apos;in &apos;meaning emerges from co-occurrence&apos; felsefesi. Pre-NN dönem (LSA, LSI) vs neural era (word2vec → BERT → LLM). Türkçe için pratik anlam.</image:caption>
      <image:title>Embedding Nedir? Token ID&apos;den Anlam Vektörüne Köprü — Discrete&apos;den Continuous&apos;a Devrim</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/embedding-nedir-token-id-vektor-koprusu</loc>
    <lastmod>2026-05-13T13:00:26.928Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/embedding-nedir-token-id-vektor-koprusu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/embedding-nedir-token-id-vektor-koprusu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/embedding-nedir-token-id-vektor-koprusu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Embedding&apos;in matematiksel anatomisi: integer token ID&apos;sini d-dimensional dense vector&apos;e mapping. Vocab × d_model matrisi. One-hot encoding&apos;in dejenere durumu. Niye semantic vector space çalışıyor (distributional hypothesis, Firth 1957). Embedding&apos;in &apos;meaning emerges from co-occurrence&apos; felsefesi. Pre-NN dönem (LSA, LSI) vs neural era (word2vec → BERT → LLM). Türkçe için pratik anlam.</image:caption>
      <image:title>Embedding Nedir? Token ID&apos;den Anlam Vektörüne Köprü — Discrete&apos;den Continuous&apos;a Devrim</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/word2vec-mikolov-2013-skip-gram-cbow-negative-sampling</loc>
    <lastmod>2026-05-13T13:00:27.017Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/word2vec-mikolov-2013-skip-gram-cbow-negative-sampling"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/word2vec-mikolov-2013-skip-gram-cbow-negative-sampling"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/word2vec-mikolov-2013-skip-gram-cbow-negative-sampling"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Mikolov 2013 paper&apos;ının satır satır anatomi: Skip-Gram vs CBOW mimari farkları, softmax computational bottleneck, hierarchical softmax (Huffman tree), negative sampling (Mikolov 2013b), subsampling, dynamic window. Pure Python implementation 100 satırda. Gensim ile Türkçe word2vec eğitim demosu. Modern LLM embedding ile karşılaştırma.</image:caption>
      <image:title>Word2Vec Satır Satır: Mikolov 2013&apos;ün Skip-Gram + CBOW + Negative Sampling Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/word2vec-mikolov-2013-skip-gram-cbow-negative-sampling</loc>
    <lastmod>2026-05-13T13:00:27.017Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/word2vec-mikolov-2013-skip-gram-cbow-negative-sampling"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/word2vec-mikolov-2013-skip-gram-cbow-negative-sampling"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/word2vec-mikolov-2013-skip-gram-cbow-negative-sampling"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Mikolov 2013 paper&apos;ının satır satır anatomi: Skip-Gram vs CBOW mimari farkları, softmax computational bottleneck, hierarchical softmax (Huffman tree), negative sampling (Mikolov 2013b), subsampling, dynamic window. Pure Python implementation 100 satırda. Gensim ile Türkçe word2vec eğitim demosu. Modern LLM embedding ile karşılaştırma.</image:caption>
      <image:title>Word2Vec Satır Satır: Mikolov 2013&apos;ün Skip-Gram + CBOW + Negative Sampling Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/glove-fasttext-global-cooccurrence-subword-extension</loc>
    <lastmod>2026-05-13T13:00:27.105Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/glove-fasttext-global-cooccurrence-subword-extension"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/glove-fasttext-global-cooccurrence-subword-extension"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/glove-fasttext-global-cooccurrence-subword-extension"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GloVe (Pennington 2014) global co-occurrence matrisi yaklaşımı vs Word2Vec local window: matematiksel formülasyon, weighted least squares objective, X_ij interpretation. FastText (Bojanowski 2017) subword n-gram embedding: &apos;merhaba&apos; = &apos;mer&apos; + &apos;erh&apos; + ... OOV problem çözümü, Türkçe morfolojik diller için ideal. Performance karşılaştırması, hangi senaryoda hangisi.</image:caption>
      <image:title>GloVe + FastText: Global Co-Occurrence Matrisi + Subword N-Gram Genişletme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/glove-fasttext-global-cooccurrence-subword-extension</loc>
    <lastmod>2026-05-13T13:00:27.105Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/glove-fasttext-global-cooccurrence-subword-extension"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/glove-fasttext-global-cooccurrence-subword-extension"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/glove-fasttext-global-cooccurrence-subword-extension"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GloVe (Pennington 2014) global co-occurrence matrisi yaklaşımı vs Word2Vec local window: matematiksel formülasyon, weighted least squares objective, X_ij interpretation. FastText (Bojanowski 2017) subword n-gram embedding: &apos;merhaba&apos; = &apos;mer&apos; + &apos;erh&apos; + ... OOV problem çözümü, Türkçe morfolojik diller için ideal. Performance karşılaştırması, hangi senaryoda hangisi.</image:caption>
      <image:title>GloVe + FastText: Global Co-Occurrence Matrisi + Subword N-Gram Genişletme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/modern-llm-embedding-tying-input-output-paylasim</loc>
    <lastmod>2026-05-13T13:00:27.195Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/modern-llm-embedding-tying-input-output-paylasim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/modern-llm-embedding-tying-input-output-paylasim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/modern-llm-embedding-tying-input-output-paylasim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modern transformer mimarisinde embedding katmanı: nn.Embedding initialization (Llama-3 style), embedding tying (input/output paylaşımı) — matematiksel justification ve memory savings, transformer pre-layernorm öncesi embedding scaling (sqrt(d_model) ya da değil), RoPE öncesi pozisyon ekleme yok, multimodal embeddings (vision + audio tokens). Llama-3, GPT-4o, Claude-3 mimari farkları.</image:caption>
      <image:title>Modern LLM Embedding Katmanı + Embedding Tying: Input/Output Paylaşımı ve Scaling</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/modern-llm-embedding-tying-input-output-paylasim</loc>
    <lastmod>2026-05-13T13:00:27.195Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/modern-llm-embedding-tying-input-output-paylasim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/modern-llm-embedding-tying-input-output-paylasim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/modern-llm-embedding-tying-input-output-paylasim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modern transformer mimarisinde embedding katmanı: nn.Embedding initialization (Llama-3 style), embedding tying (input/output paylaşımı) — matematiksel justification ve memory savings, transformer pre-layernorm öncesi embedding scaling (sqrt(d_model) ya da değil), RoPE öncesi pozisyon ekleme yok, multimodal embeddings (vision + audio tokens). Llama-3, GPT-4o, Claude-3 mimari farkları.</image:caption>
      <image:title>Modern LLM Embedding Katmanı + Embedding Tying: Input/Output Paylaşımı ve Scaling</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/embedding-geometry-cosine-isotropy-bertology</loc>
    <lastmod>2026-05-13T13:00:27.289Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/embedding-geometry-cosine-isotropy-bertology"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/embedding-geometry-cosine-isotropy-bertology"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/embedding-geometry-cosine-isotropy-bertology"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Embedding vector space&apos;in topolojisi: cosine similarity vs Euclidean distance vs dot product (hangisi ne zaman, matematiksel ilişkiler), isotropy (vectors balanced across directions, Gao 2019 &apos;representation degeneration&apos;), anisotropy problemi BERT/GPT embeddings&apos;de, mitigation (whitening, normalization). BERTology bulguları: hangi layer&apos;da hangi bilgi (Rogers 2020). Türkçe için pratik analiz.</image:caption>
      <image:title>Embedding Geometry: Cosine Similarity, Euclidean Distance, Isotropy ve BERTology Bulguları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/embedding-geometry-cosine-isotropy-bertology</loc>
    <lastmod>2026-05-13T13:00:27.289Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/embedding-geometry-cosine-isotropy-bertology"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/embedding-geometry-cosine-isotropy-bertology"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/embedding-geometry-cosine-isotropy-bertology"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Embedding vector space&apos;in topolojisi: cosine similarity vs Euclidean distance vs dot product (hangisi ne zaman, matematiksel ilişkiler), isotropy (vectors balanced across directions, Gao 2019 &apos;representation degeneration&apos;), anisotropy problemi BERT/GPT embeddings&apos;de, mitigation (whitening, normalization). BERTology bulguları: hangi layer&apos;da hangi bilgi (Rogers 2020). Türkçe için pratik analiz.</image:caption>
      <image:title>Embedding Geometry: Cosine Similarity, Euclidean Distance, Isotropy ve BERTology Bulguları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-semantic-search-faiss-mini-rag</loc>
    <lastmod>2026-05-13T13:03:51.177Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-semantic-search-faiss-mini-rag"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-semantic-search-faiss-mini-rag"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-semantic-search-faiss-mini-rag"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 7&apos;nin capstone projesi: Türkçe semantic search sistemi sıfırdan. sentence-transformers Türkçe model seçimi, FAISS vector index, production-grade query pipeline, mini-RAG architecture (retriever + generator), benchmark + deployment. Embedding teorisinin pratik uygulaması.</image:caption>
      <image:title>Capstone Modül 7: Türkçe Semantic Search Sistemi — sentence-transformers + FAISS + Mini-RAG</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-semantic-search-faiss-mini-rag</loc>
    <lastmod>2026-05-13T13:03:51.177Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-semantic-search-faiss-mini-rag"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-semantic-search-faiss-mini-rag"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-semantic-search-faiss-mini-rag"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 7&apos;nin capstone projesi: Türkçe semantic search sistemi sıfırdan. sentence-transformers Türkçe model seçimi, FAISS vector index, production-grade query pipeline, mini-RAG architecture (retriever + generator), benchmark + deployment. Embedding teorisinin pratik uygulaması.</image:caption>
      <image:title>Capstone Modül 7: Türkçe Semantic Search Sistemi — sentence-transformers + FAISS + Mini-RAG</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/scaled-dot-product-attention-vaswani-2017-qkv</loc>
    <lastmod>2026-05-13T13:00:27.474Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/scaled-dot-product-attention-vaswani-2017-qkv"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/scaled-dot-product-attention-vaswani-2017-qkv"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/scaled-dot-product-attention-vaswani-2017-qkv"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Transformer&apos;ın temel taşı — scaled dot-product attention&apos;ın matematiksel anatomisi: Query/Key/Value üçlüsü, dot product similarity, softmax normalize, sqrt(d_k) scaling justification, causal mask (autoregressive), attention weights interpretation. PyTorch implementation, FLOP analizi, numerical stability concerns, Türkçe örneklerle attention pattern görselleştirme.</image:caption>
      <image:title>Scaled Dot-Product Attention: Vaswani 2017&apos;nin Kalbi Satır Satır — Query, Key, Value Üçlüsünün Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/scaled-dot-product-attention-vaswani-2017-qkv</loc>
    <lastmod>2026-05-13T13:00:27.474Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/scaled-dot-product-attention-vaswani-2017-qkv"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/scaled-dot-product-attention-vaswani-2017-qkv"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/scaled-dot-product-attention-vaswani-2017-qkv"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Transformer&apos;ın temel taşı — scaled dot-product attention&apos;ın matematiksel anatomisi: Query/Key/Value üçlüsü, dot product similarity, softmax normalize, sqrt(d_k) scaling justification, causal mask (autoregressive), attention weights interpretation. PyTorch implementation, FLOP analizi, numerical stability concerns, Türkçe örneklerle attention pattern görselleştirme.</image:caption>
      <image:title>Scaled Dot-Product Attention: Vaswani 2017&apos;nin Kalbi Satır Satır — Query, Key, Value Üçlüsünün Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/multi-head-attention-gqa-mqa-llama-3</loc>
    <lastmod>2026-05-13T13:00:27.562Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/multi-head-attention-gqa-mqa-llama-3"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/multi-head-attention-gqa-mqa-llama-3"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tek attention&apos;ı niye N paralel head&apos;e bölüyoruz: her head&apos;in farklı pattern öğrenme kapasitesi (syntactic, semantic, positional). Concat + output projection mimari, head pruning empirical bulgular, Llama-3 grouped-query attention (GQA), Mistral multi-query attention (MQA), head visualization Türkçe örneklerle.</image:caption>
      <image:title>Multi-Head Attention: N Paralel Head, Concat + Projection, Grouped-Query Attention (GQA), Multi-Query Attention (MQA)</image:title>
    </image:image>
  </url>
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    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/multi-head-attention-gqa-mqa-llama-3</loc>
    <lastmod>2026-05-13T13:00:27.562Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/multi-head-attention-gqa-mqa-llama-3"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/multi-head-attention-gqa-mqa-llama-3"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tek attention&apos;ı niye N paralel head&apos;e bölüyoruz: her head&apos;in farklı pattern öğrenme kapasitesi (syntactic, semantic, positional). Concat + output projection mimari, head pruning empirical bulgular, Llama-3 grouped-query attention (GQA), Mistral multi-query attention (MQA), head visualization Türkçe örneklerle.</image:caption>
      <image:title>Multi-Head Attention: N Paralel Head, Concat + Projection, Grouped-Query Attention (GQA), Multi-Query Attention (MQA)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/flashattention-dao-2022-io-aware-attention</loc>
    <lastmod>2026-05-13T13:00:27.650Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/flashattention-dao-2022-io-aware-attention"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/flashattention-dao-2022-io-aware-attention"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/flashattention-dao-2022-io-aware-attention"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>FlashAttention&apos;ın matematiksel ve sistemsel anatomi: niye standard attention memory-bound, GPU memory hierarchy (HBM vs SRAM), tile-based computation, online softmax, recomputation backward. FlashAttention-1 (Dao 2022), FlashAttention-2, FlashAttention-3 evrimi. PyTorch flash_attn library, performance benchmarks, long context enablement.</image:caption>
      <image:title>FlashAttention: IO-Aware Attention — Dao 2022 Algoritması ve Modern Implementations</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/flashattention-dao-2022-io-aware-attention</loc>
    <lastmod>2026-05-13T13:00:27.650Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/flashattention-dao-2022-io-aware-attention"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/flashattention-dao-2022-io-aware-attention"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/flashattention-dao-2022-io-aware-attention"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>FlashAttention&apos;ın matematiksel ve sistemsel anatomi: niye standard attention memory-bound, GPU memory hierarchy (HBM vs SRAM), tile-based computation, online softmax, recomputation backward. FlashAttention-1 (Dao 2022), FlashAttention-2, FlashAttention-3 evrimi. PyTorch flash_attn library, performance benchmarks, long context enablement.</image:caption>
      <image:title>FlashAttention: IO-Aware Attention — Dao 2022 Algoritması ve Modern Implementations</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/kv-cache-paged-attention-vllm-continuous-batching</loc>
    <lastmod>2026-05-13T13:00:27.737Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/kv-cache-paged-attention-vllm-continuous-batching"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/kv-cache-paged-attention-vllm-continuous-batching"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/kv-cache-paged-attention-vllm-continuous-batching"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM inference serving optimization: KV cache anatomy (prefill vs decode phases), memory fragmentation problem, paged attention (vLLM 2023 Kwon), continuous batching, dynamic memory allocation. Llama-3 production serving math: throughput, latency trade-offs, multi-tenancy.</image:caption>
      <image:title>KV Cache + Paged Attention: Inference Serving Optimization — vLLM Paged Attention ve Continuous Batching</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/kv-cache-paged-attention-vllm-continuous-batching</loc>
    <lastmod>2026-05-13T13:00:27.737Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/kv-cache-paged-attention-vllm-continuous-batching"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/kv-cache-paged-attention-vllm-continuous-batching"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/kv-cache-paged-attention-vllm-continuous-batching"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM inference serving optimization: KV cache anatomy (prefill vs decode phases), memory fragmentation problem, paged attention (vLLM 2023 Kwon), continuous batching, dynamic memory allocation. Llama-3 production serving math: throughput, latency trade-offs, multi-tenancy.</image:caption>
      <image:title>KV Cache + Paged Attention: Inference Serving Optimization — vLLM Paged Attention ve Continuous Batching</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-linear-attention-retnet-mamba-ssm</loc>
    <lastmod>2026-05-13T13:00:27.826Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-linear-attention-retnet-mamba-ssm"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-linear-attention-retnet-mamba-ssm"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-linear-attention-retnet-mamba-ssm"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 8 capstone: quadratic attention&apos;ın alternatifleri. Linear Attention (Katharopoulos 2020) — kernel trick + recurrent form. RetNet (Sun 2023) — retention mechanism Microsoft. Mamba (Gu Dao 2023) — selective state space models. Hangi sub-quadratic mimari hangi senaryo için, GPT-4 vs Mamba karşılaştırma, hibrit modeller (Jamba), gelecek trendleri.</image:caption>
      <image:title>Capstone Modül 8: Quadratic Attention&apos;a Alternatifler — Linear Attention, RetNet, Mamba (State Space Models)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-linear-attention-retnet-mamba-ssm</loc>
    <lastmod>2026-05-13T13:00:27.826Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-linear-attention-retnet-mamba-ssm"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-linear-attention-retnet-mamba-ssm"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-linear-attention-retnet-mamba-ssm"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 8 capstone: quadratic attention&apos;ın alternatifleri. Linear Attention (Katharopoulos 2020) — kernel trick + recurrent form. RetNet (Sun 2023) — retention mechanism Microsoft. Mamba (Gu Dao 2023) — selective state space models. Hangi sub-quadratic mimari hangi senaryo için, GPT-4 vs Mamba karşılaştırma, hibrit modeller (Jamba), gelecek trendleri.</image:caption>
      <image:title>Capstone Modül 8: Quadratic Attention&apos;a Alternatifler — Linear Attention, RetNet, Mamba (State Space Models)</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/position-encoding-sinusoidal-learned-absolute</loc>
    <lastmod>2026-05-13T13:00:27.915Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/position-encoding-sinusoidal-learned-absolute"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/position-encoding-sinusoidal-learned-absolute"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/position-encoding-sinusoidal-learned-absolute"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Attention&apos;ın permutation-invariance problemi: &apos;Köpek kediyi ısırdı&apos; ile &apos;Kedi köpeği ısırdı&apos; aynı! Position encoding&apos;in zorunluluğu. Vaswani 2017 sinusoidal formülü (sin/cos farklı frequency&apos;lerde), generalization argümanı (longer sequences). GPT-2 learned absolute position embedding, max_position_embeddings sınırı. Trade-offs, Türkçe sözdizimi için pratik anlamı.</image:caption>
      <image:title>Position Encoding Neden Zorunlu? Sinusoidal vs Learned Absolute Position — Vaswani 2017&apos;den GPT-2&apos;ye Klasik Yaklaşımlar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/position-encoding-sinusoidal-learned-absolute</loc>
    <lastmod>2026-05-13T13:00:27.915Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/position-encoding-sinusoidal-learned-absolute"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/position-encoding-sinusoidal-learned-absolute"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/position-encoding-sinusoidal-learned-absolute"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Attention&apos;ın permutation-invariance problemi: &apos;Köpek kediyi ısırdı&apos; ile &apos;Kedi köpeği ısırdı&apos; aynı! Position encoding&apos;in zorunluluğu. Vaswani 2017 sinusoidal formülü (sin/cos farklı frequency&apos;lerde), generalization argümanı (longer sequences). GPT-2 learned absolute position embedding, max_position_embeddings sınırı. Trade-offs, Türkçe sözdizimi için pratik anlamı.</image:caption>
      <image:title>Position Encoding Neden Zorunlu? Sinusoidal vs Learned Absolute Position — Vaswani 2017&apos;den GPT-2&apos;ye Klasik Yaklaşımlar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/rope-rotary-position-embedding-su-2021-llama-3</loc>
    <lastmod>2026-05-13T13:00:27.999Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/rope-rotary-position-embedding-su-2021-llama-3"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/rope-rotary-position-embedding-su-2021-llama-3"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/rope-rotary-position-embedding-su-2021-llama-3"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>RoPE&apos;in matematiksel anatomisi: kompleks sayı rotation interpretation, niye Q ve K&apos;ye uygulanır, relative position implicit derivation. Llama-3 RoPE implementation satır satır, base frequency 10000, pair-wise rotation. PyTorch implementation, RoPE vs sinusoidal/learned karşılaştırma, modern modellerin yaygın tercih sebebi.</image:caption>
      <image:title>RoPE Derinlemesine: Rotary Position Embedding&apos;in Matematiksel Anatomisi — Su 2021&apos;den Llama-3&apos;e</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/rope-rotary-position-embedding-su-2021-llama-3</loc>
    <lastmod>2026-05-13T13:00:27.999Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/rope-rotary-position-embedding-su-2021-llama-3"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/rope-rotary-position-embedding-su-2021-llama-3"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/rope-rotary-position-embedding-su-2021-llama-3"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>RoPE&apos;in matematiksel anatomisi: kompleks sayı rotation interpretation, niye Q ve K&apos;ye uygulanır, relative position implicit derivation. Llama-3 RoPE implementation satır satır, base frequency 10000, pair-wise rotation. PyTorch implementation, RoPE vs sinusoidal/learned karşılaştırma, modern modellerin yaygın tercih sebebi.</image:caption>
      <image:title>RoPE Derinlemesine: Rotary Position Embedding&apos;in Matematiksel Anatomisi — Su 2021&apos;den Llama-3&apos;e</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/alibi-attention-linear-biases-press-2021</loc>
    <lastmod>2026-05-13T13:00:28.086Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/alibi-attention-linear-biases-press-2021"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/alibi-attention-linear-biases-press-2021"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/alibi-attention-linear-biases-press-2021"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ALiBi (Press 2021): position embedding kullanmadan attention score&apos;a linear bias ekleyerek pozisyon bilgisini inject etmek. Math: attention[i,j] += m × (j-i). Per-head slopes hierarchy (m_h = 2^{-8h/H}). Strengths: zero parameters, train-short eval-long extrapolation, simple implementation. RoPE ile karşılaştırma, Mistral ve BLOOM kullanımı.</image:caption>
      <image:title>ALiBi: Attention with Linear Biases — Press 2021&apos;in Sade Çözümü ve Extrapolation Avantajı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/alibi-attention-linear-biases-press-2021</loc>
    <lastmod>2026-05-13T13:00:28.086Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/alibi-attention-linear-biases-press-2021"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/alibi-attention-linear-biases-press-2021"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/alibi-attention-linear-biases-press-2021"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ALiBi (Press 2021): position embedding kullanmadan attention score&apos;a linear bias ekleyerek pozisyon bilgisini inject etmek. Math: attention[i,j] += m × (j-i). Per-head slopes hierarchy (m_h = 2^{-8h/H}). Strengths: zero parameters, train-short eval-long extrapolation, simple implementation. RoPE ile karşılaştırma, Mistral ve BLOOM kullanımı.</image:caption>
      <image:title>ALiBi: Attention with Linear Biases — Press 2021&apos;in Sade Çözümü ve Extrapolation Avantajı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/long-context-ntk-aware-yarn-longrope-extrapolation</loc>
    <lastmod>2026-05-13T13:00:28.174Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/long-context-ntk-aware-yarn-longrope-extrapolation"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/long-context-ntk-aware-yarn-longrope-extrapolation"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/long-context-ntk-aware-yarn-longrope-extrapolation"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>RoPE&apos;in long context&apos;e genişletilmesi: NTK-aware scaling intuisyonu, YaRN (Peng 2023) — kapsamlı çözüm + temperature scaling, LongRoPE (Microsoft 2024) — 2M token context. Llama-3-8B base 8K → 128K extension reciplerine, Gemini 1.5 1M token tricks, fine-tune protokolü.</image:caption>
      <image:title>Long Context Extrapolation: NTK-Aware Scaling + YaRN + LongRoPE — 8K&apos;dan 1M Token&apos;a Yolculuk</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/long-context-ntk-aware-yarn-longrope-extrapolation</loc>
    <lastmod>2026-05-13T13:00:28.174Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/long-context-ntk-aware-yarn-longrope-extrapolation"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/long-context-ntk-aware-yarn-longrope-extrapolation"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/long-context-ntk-aware-yarn-longrope-extrapolation"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>RoPE&apos;in long context&apos;e genişletilmesi: NTK-aware scaling intuisyonu, YaRN (Peng 2023) — kapsamlı çözüm + temperature scaling, LongRoPE (Microsoft 2024) — 2M token context. Llama-3-8B base 8K → 128K extension reciplerine, Gemini 1.5 1M token tricks, fine-tune protokolü.</image:caption>
      <image:title>Long Context Extrapolation: NTK-Aware Scaling + YaRN + LongRoPE — 8K&apos;dan 1M Token&apos;a Yolculuk</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-llama-3-rope-50-line-implementation</loc>
    <lastmod>2026-05-13T13:00:28.262Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-llama-3-rope-50-line-implementation"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-llama-3-rope-50-line-implementation"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-llama-3-rope-50-line-implementation"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 9 capstone: Llama-3 uyumlu RoPE&apos;i 50 satır pure NumPy&apos;da implement et. cos/sin cache precomputation, pair-wise rotation, position visualization (cos/sin heatmap, attention bias pattern). Llama-3 actual weights ile compatibility test. Türkçe örneklerle position pattern interpretasyonu.</image:caption>
      <image:title>Capstone Modül 9: Llama-3 RoPE&apos;i 50 Satırda Sıfırdan Implement Et — Pure NumPy + Visualization</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-llama-3-rope-50-line-implementation</loc>
    <lastmod>2026-05-13T13:00:28.262Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-llama-3-rope-50-line-implementation"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-llama-3-rope-50-line-implementation"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-llama-3-rope-50-line-implementation"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 9 capstone: Llama-3 uyumlu RoPE&apos;i 50 satır pure NumPy&apos;da implement et. cos/sin cache precomputation, pair-wise rotation, position visualization (cos/sin heatmap, attention bias pattern). Llama-3 actual weights ile compatibility test. Türkçe örneklerle position pattern interpretasyonu.</image:caption>
      <image:title>Capstone Modül 9: Llama-3 RoPE&apos;i 50 Satırda Sıfırdan Implement Et — Pure NumPy + Visualization</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/layernorm-rmsnorm-pre-ln-post-ln</loc>
    <lastmod>2026-05-13T13:00:28.349Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/layernorm-rmsnorm-pre-ln-post-ln"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/layernorm-rmsnorm-pre-ln-post-ln"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/layernorm-rmsnorm-pre-ln-post-ln"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Transformer training stabilitesinin matematiksel ve sistemsel anatomi: LayerNorm (Ba 2016) klasik formülü, RMSNorm (Zhang 2019) — Llama-3 tercihi, niye gain parameter only, computational savings. Pre-LN (modern) vs Post-LN (original Vaswani) trade-off, gradient flow, deep transformer stability. Türkçe model fine-tune&apos;da normalization concerns.</image:caption>
      <image:title>Normalization Devrim: LayerNorm, RMSNorm ve Pre-LN vs Post-LN — Training Stabilitesinin Temel Taşı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/layernorm-rmsnorm-pre-ln-post-ln</loc>
    <lastmod>2026-05-13T13:00:28.349Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/layernorm-rmsnorm-pre-ln-post-ln"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/layernorm-rmsnorm-pre-ln-post-ln"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/layernorm-rmsnorm-pre-ln-post-ln"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Transformer training stabilitesinin matematiksel ve sistemsel anatomi: LayerNorm (Ba 2016) klasik formülü, RMSNorm (Zhang 2019) — Llama-3 tercihi, niye gain parameter only, computational savings. Pre-LN (modern) vs Post-LN (original Vaswani) trade-off, gradient flow, deep transformer stability. Türkçe model fine-tune&apos;da normalization concerns.</image:caption>
      <image:title>Normalization Devrim: LayerNorm, RMSNorm ve Pre-LN vs Post-LN — Training Stabilitesinin Temel Taşı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/swiglu-activation-shazeer-2020-llama-3</loc>
    <lastmod>2026-05-13T13:00:28.436Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/swiglu-activation-shazeer-2020-llama-3"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/swiglu-activation-shazeer-2020-llama-3"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/swiglu-activation-shazeer-2020-llama-3"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>SwiGLU activation function&apos;ın anatomi: SiLU (Sigmoid-weighted Linear Unit) base + Gated Linear Unit mechanism. Shazeer 2020 &apos;GLU Variants Improve Transformer&apos;. ReLU/GeLU karşılaştırma, niye modern modellerin tercihi. FFN dimensions (d_ff = 8/3 × d_model Llama-3 tercihi), parameter math, Llama-3 implementation.</image:caption>
      <image:title>SwiGLU Activation: SiLU + GLU = Modern FFN&apos;in Kalbi — Shazeer 2020&apos;den Llama-3&apos;e</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/swiglu-activation-shazeer-2020-llama-3</loc>
    <lastmod>2026-05-13T13:00:28.436Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/swiglu-activation-shazeer-2020-llama-3"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/swiglu-activation-shazeer-2020-llama-3"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/swiglu-activation-shazeer-2020-llama-3"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>SwiGLU activation function&apos;ın anatomi: SiLU (Sigmoid-weighted Linear Unit) base + Gated Linear Unit mechanism. Shazeer 2020 &apos;GLU Variants Improve Transformer&apos;. ReLU/GeLU karşılaştırma, niye modern modellerin tercihi. FFN dimensions (d_ff = 8/3 × d_model Llama-3 tercihi), parameter math, Llama-3 implementation.</image:caption>
      <image:title>SwiGLU Activation: SiLU + GLU = Modern FFN&apos;in Kalbi — Shazeer 2020&apos;den Llama-3&apos;e</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-llama-3-transformer-block-200-lines</loc>
    <lastmod>2026-05-13T13:00:28.525Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-llama-3-transformer-block-200-lines"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-llama-3-transformer-block-200-lines"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-llama-3-transformer-block-200-lines"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 10 capstone: Llama-3 architecture transformer block&apos;unu 200 satırda implement et. RMSNorm + Pre-LN + GQA (Grouped-Query Attention) + RoPE + SwiGLU FFN + residual connections. Module 6-10&apos;un sentezi. Türkçe örnekle forward pass, gradient flow analysis, Llama-3 actual weights load test.</image:caption>
      <image:title>Capstone Modül 10: Llama-3 Transformer Block&apos;u 200 Satırda Sıfırdan — RMSNorm + RoPE + GQA + SwiGLU</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-llama-3-transformer-block-200-lines</loc>
    <lastmod>2026-05-13T13:00:28.525Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-llama-3-transformer-block-200-lines"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-llama-3-transformer-block-200-lines"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-llama-3-transformer-block-200-lines"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 10 capstone: Llama-3 architecture transformer block&apos;unu 200 satırda implement et. RMSNorm + Pre-LN + GQA (Grouped-Query Attention) + RoPE + SwiGLU FFN + residual connections. Module 6-10&apos;un sentezi. Türkçe örnekle forward pass, gradient flow analysis, Llama-3 actual weights load test.</image:caption>
      <image:title>Capstone Modül 10: Llama-3 Transformer Block&apos;u 200 Satırda Sıfırdan — RMSNorm + RoPE + GQA + SwiGLU</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/pretraining-pipeline-corpus-tokenize-pack-train</loc>
    <lastmod>2026-05-13T13:00:28.616Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/pretraining-pipeline-corpus-tokenize-pack-train"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/pretraining-pipeline-corpus-tokenize-pack-train"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/pretraining-pipeline-corpus-tokenize-pack-train"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1623282033815-40b05d96c903?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Pre-training pipeline&apos;ın tüm aşamaları: corpus collection (Common Crawl, Wikipedia, code), data cleaning (deduplication, language filtering, quality scoring), tokenization batching, sequence packing strategy, document boundary handling. Llama-3 production recipe: 15T tokens, 24K H100 günü compute, 70 günde training.</image:caption>
      <image:title>Pre-training Pipeline End-to-End: Corpus → Tokenize → Pack → Train — Llama-3 Production Recipe</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/pretraining-pipeline-corpus-tokenize-pack-train</loc>
    <lastmod>2026-05-13T13:00:28.616Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/pretraining-pipeline-corpus-tokenize-pack-train"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/pretraining-pipeline-corpus-tokenize-pack-train"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/pretraining-pipeline-corpus-tokenize-pack-train"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1623282033815-40b05d96c903?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Pre-training pipeline&apos;ın tüm aşamaları: corpus collection (Common Crawl, Wikipedia, code), data cleaning (deduplication, language filtering, quality scoring), tokenization batching, sequence packing strategy, document boundary handling. Llama-3 production recipe: 15T tokens, 24K H100 günü compute, 70 günde training.</image:caption>
      <image:title>Pre-training Pipeline End-to-End: Corpus → Tokenize → Pack → Train — Llama-3 Production Recipe</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/adamw-optimizer-learning-rate-schedule</loc>
    <lastmod>2026-05-13T13:00:28.705Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/adamw-optimizer-learning-rate-schedule"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/adamw-optimizer-learning-rate-schedule"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/adamw-optimizer-learning-rate-schedule"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modern LLM optimization: SGD&apos;den Adam&apos;a, Adam&apos;dan AdamW&apos;ye evrim. Loshchilov 2019 weight decay decoupling. Momentum (β1=0.9) + variance estimate (β2=0.95) intuition. Learning rate schedules: cosine decay, linear decay, warmup gerekli. Gradient clipping, mixed precision training, hyperparameter pitfalls.</image:caption>
      <image:title>AdamW + Learning Rate Schedule: Modern LLM Optimization&apos;ın Matematik Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/adamw-optimizer-learning-rate-schedule</loc>
    <lastmod>2026-05-13T13:00:28.705Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/adamw-optimizer-learning-rate-schedule"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/adamw-optimizer-learning-rate-schedule"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/adamw-optimizer-learning-rate-schedule"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modern LLM optimization: SGD&apos;den Adam&apos;a, Adam&apos;dan AdamW&apos;ye evrim. Loshchilov 2019 weight decay decoupling. Momentum (β1=0.9) + variance estimate (β2=0.95) intuition. Learning rate schedules: cosine decay, linear decay, warmup gerekli. Gradient clipping, mixed precision training, hyperparameter pitfalls.</image:caption>
      <image:title>AdamW + Learning Rate Schedule: Modern LLM Optimization&apos;ın Matematik Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-mini-llama-3-pretraining-single-h100</loc>
    <lastmod>2026-05-13T13:00:28.795Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-mini-llama-3-pretraining-single-h100"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-mini-llama-3-pretraining-single-h100"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-mini-llama-3-pretraining-single-h100"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 11 capstone: kendi Llama-3 architecture mini model&apos;i (100M param) sıfırdan pre-train. Modül 6-10&apos;un tüm parçaları (Llama tokenizer + RMSNorm + GQA + RoPE + SwiGLU) + Modül 11 pre-training pipeline + AdamW. 5GB Türkçe corpus, single H100, 1 hafta. Validation loss tracking, checkpoint, sampling demosu.</image:caption>
      <image:title>Capstone Modül 11: Mini Llama-3 100M Param Pre-training — Single H100, 1 Hafta</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-mini-llama-3-pretraining-single-h100</loc>
    <lastmod>2026-05-13T13:00:28.795Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-mini-llama-3-pretraining-single-h100"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-mini-llama-3-pretraining-single-h100"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-mini-llama-3-pretraining-single-h100"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 11 capstone: kendi Llama-3 architecture mini model&apos;i (100M param) sıfırdan pre-train. Modül 6-10&apos;un tüm parçaları (Llama tokenizer + RMSNorm + GQA + RoPE + SwiGLU) + Modül 11 pre-training pipeline + AdamW. 5GB Türkçe corpus, single H100, 1 hafta. Validation loss tracking, checkpoint, sampling demosu.</image:caption>
      <image:title>Capstone Modül 11: Mini Llama-3 100M Param Pre-training — Single H100, 1 Hafta</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/kaplan-scaling-laws-2020-power-law</loc>
    <lastmod>2026-05-13T13:00:28.882Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/kaplan-scaling-laws-2020-power-law"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/kaplan-scaling-laws-2020-power-law"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/kaplan-scaling-laws-2020-power-law"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Kaplan et al. 2020 paper&apos;ının anatomi: LLM loss compute (C), parameters (N), data (D) için power law&apos;a uyar. Niye log-log plot lineer, optimum allocation formülü, &apos;bigger is better&apos; iddiası, GPT-3 (175B) bunun üzerine inşa edildi. Limitleri ve sonraki Chinchilla refutation&apos;ı.</image:caption>
      <image:title>Kaplan Scaling Laws (2020): LLM Performansının Power Law Anatomisi — Compute, Data, Param Üçgeni</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/kaplan-scaling-laws-2020-power-law</loc>
    <lastmod>2026-05-13T13:00:28.882Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/kaplan-scaling-laws-2020-power-law"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/kaplan-scaling-laws-2020-power-law"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/kaplan-scaling-laws-2020-power-law"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Kaplan et al. 2020 paper&apos;ının anatomi: LLM loss compute (C), parameters (N), data (D) için power law&apos;a uyar. Niye log-log plot lineer, optimum allocation formülü, &apos;bigger is better&apos; iddiası, GPT-3 (175B) bunun üzerine inşa edildi. Limitleri ve sonraki Chinchilla refutation&apos;ı.</image:caption>
      <image:title>Kaplan Scaling Laws (2020): LLM Performansının Power Law Anatomisi — Compute, Data, Param Üçgeni</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/chinchilla-scaling-laws-hoffmann-2022</loc>
    <lastmod>2026-05-13T13:00:28.967Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/chinchilla-scaling-laws-hoffmann-2022"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/chinchilla-scaling-laws-hoffmann-2022"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/chinchilla-scaling-laws-hoffmann-2022"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Hoffmann et al. 2022 &apos;Training Compute-Optimal LLMs&apos; paper&apos;ı — Kaplan&apos;ı düzeltti. Kaplan undertrained models bias. Chinchilla recipe: N ≈ D (1:1 ratio). 70B Chinchilla model &gt; 280B Gopher (Hoffmann). Llama-3 Chinchilla-aware. Compute-optimal formula yeni, post-Chinchilla overtraining trend.</image:caption>
      <image:title>Chinchilla Scaling Laws (2022): Hoffmann et al. — 1:1 Param:Data Devrim</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/chinchilla-scaling-laws-hoffmann-2022</loc>
    <lastmod>2026-05-13T13:00:28.967Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/chinchilla-scaling-laws-hoffmann-2022"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/chinchilla-scaling-laws-hoffmann-2022"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/chinchilla-scaling-laws-hoffmann-2022"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Hoffmann et al. 2022 &apos;Training Compute-Optimal LLMs&apos; paper&apos;ı — Kaplan&apos;ı düzeltti. Kaplan undertrained models bias. Chinchilla recipe: N ≈ D (1:1 ratio). 70B Chinchilla model &gt; 280B Gopher (Hoffmann). Llama-3 Chinchilla-aware. Compute-optimal formula yeni, post-Chinchilla overtraining trend.</image:caption>
      <image:title>Chinchilla Scaling Laws (2022): Hoffmann et al. — 1:1 Param:Data Devrim</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-compute-budget-planner-chinchilla</loc>
    <lastmod>2026-05-13T13:00:29.050Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-compute-budget-planner-chinchilla"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-compute-budget-planner-chinchilla"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-compute-budget-planner-chinchilla"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 12 capstone: Kendi LLM training budget&apos;ı planla. Hedeflediğin model size (1B-70B), available compute (single GPU / cluster), available data — Chinchilla-aware optimal allocation hesapla. Cost estimator ($/training), time estimator, quality projection.</image:caption>
      <image:title>Capstone Modül 12: Kendi LLM Training Compute Budget&apos;ını Planla — Chinchilla-Aware Calculator</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-compute-budget-planner-chinchilla</loc>
    <lastmod>2026-05-13T13:00:29.050Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-compute-budget-planner-chinchilla"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-compute-budget-planner-chinchilla"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-compute-budget-planner-chinchilla"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 12 capstone: Kendi LLM training budget&apos;ı planla. Hedeflediğin model size (1B-70B), available compute (single GPU / cluster), available data — Chinchilla-aware optimal allocation hesapla. Cost estimator ($/training), time estimator, quality projection.</image:caption>
      <image:title>Capstone Modül 12: Kendi LLM Training Compute Budget&apos;ını Planla — Chinchilla-Aware Calculator</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/ddp-data-parallel-allreduce-nccl</loc>
    <lastmod>2026-05-13T13:00:29.136Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ddp-data-parallel-allreduce-nccl"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/ddp-data-parallel-allreduce-nccl"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ddp-data-parallel-allreduce-nccl"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Distributed Data Parallel (DDP) anatomi: model replication across GPUs, mini-batch split, forward/backward independent per GPU, gradient AllReduce synchronization. NCCL (NVIDIA Collective Communication Library), ring-allreduce algorithm, bandwidth math. PyTorch DDP API, launch scripts, common pitfalls (uneven batches, batch norm sync).</image:caption>
      <image:title>Data Parallelism (DDP): Multi-GPU LLM Training&apos;in Temeli — AllReduce ve NCCL Anatomi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/ddp-data-parallel-allreduce-nccl</loc>
    <lastmod>2026-05-13T13:00:29.136Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ddp-data-parallel-allreduce-nccl"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/ddp-data-parallel-allreduce-nccl"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ddp-data-parallel-allreduce-nccl"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Distributed Data Parallel (DDP) anatomi: model replication across GPUs, mini-batch split, forward/backward independent per GPU, gradient AllReduce synchronization. NCCL (NVIDIA Collective Communication Library), ring-allreduce algorithm, bandwidth math. PyTorch DDP API, launch scripts, common pitfalls (uneven batches, batch norm sync).</image:caption>
      <image:title>Data Parallelism (DDP): Multi-GPU LLM Training&apos;in Temeli — AllReduce ve NCCL Anatomi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/fsdp-zero-sharded-training-rajbhandari-2020</loc>
    <lastmod>2026-05-13T13:00:29.222Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/fsdp-zero-sharded-training-rajbhandari-2020"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/fsdp-zero-sharded-training-rajbhandari-2020"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/fsdp-zero-sharded-training-rajbhandari-2020"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ZeRO (Zero Redundancy Optimizer, Rajbhandari 2020) — DeepSpeed library: optimizer state, gradients, parameters sharding stage 1/2/3. FSDP (Fully Sharded Data Parallel, PyTorch native) — ZeRO-3 implementation. Llama-3 production: FSDP + activation checkpointing. Memory math: 8B model 1 H100&apos;de eğitilebilir.</image:caption>
      <image:title>FSDP + ZeRO: Sharded Training — Rajbhandari 2020&apos;den Llama-3&apos;e Memory Devrim</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/fsdp-zero-sharded-training-rajbhandari-2020</loc>
    <lastmod>2026-05-13T13:00:29.222Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/fsdp-zero-sharded-training-rajbhandari-2020"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/fsdp-zero-sharded-training-rajbhandari-2020"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/fsdp-zero-sharded-training-rajbhandari-2020"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>ZeRO (Zero Redundancy Optimizer, Rajbhandari 2020) — DeepSpeed library: optimizer state, gradients, parameters sharding stage 1/2/3. FSDP (Fully Sharded Data Parallel, PyTorch native) — ZeRO-3 implementation. Llama-3 production: FSDP + activation checkpointing. Memory math: 8B model 1 H100&apos;de eğitilebilir.</image:caption>
      <image:title>FSDP + ZeRO: Sharded Training — Rajbhandari 2020&apos;den Llama-3&apos;e Memory Devrim</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/3d-parallelism-tensor-pipeline-data-llama-3-70b</loc>
    <lastmod>2026-05-13T13:00:29.310Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/3d-parallelism-tensor-pipeline-data-llama-3-70b"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/3d-parallelism-tensor-pipeline-data-llama-3-70b"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/3d-parallelism-tensor-pipeline-data-llama-3-70b"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Frontier LLM training: Megatron-LM&apos;in 3D parallelism. Tensor Parallelism (Shoeybi 2019) — matrix splits across GPUs. Pipeline Parallelism (Huang 2018) — layer splits + bubble optimization. Combined 3D: DP × TP × PP. Llama-3 70B (DP=192, TP=8, PP=16). Communication patterns, optimization, capstone implementation outline.</image:caption>
      <image:title>3D Parallelism: Tensor + Pipeline + Data Parallel — Llama-3 70B ve 405B Training</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/3d-parallelism-tensor-pipeline-data-llama-3-70b</loc>
    <lastmod>2026-05-13T13:00:29.310Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/3d-parallelism-tensor-pipeline-data-llama-3-70b"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/3d-parallelism-tensor-pipeline-data-llama-3-70b"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/3d-parallelism-tensor-pipeline-data-llama-3-70b"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Frontier LLM training: Megatron-LM&apos;in 3D parallelism. Tensor Parallelism (Shoeybi 2019) — matrix splits across GPUs. Pipeline Parallelism (Huang 2018) — layer splits + bubble optimization. Combined 3D: DP × TP × PP. Llama-3 70B (DP=192, TP=8, PP=16). Communication patterns, optimization, capstone implementation outline.</image:caption>
      <image:title>3D Parallelism: Tensor + Pipeline + Data Parallel — Llama-3 70B ve 405B Training</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/supervised-finetuning-sft-base-to-instruct</loc>
    <lastmod>2026-05-13T13:00:29.393Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/supervised-finetuning-sft-base-to-instruct"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/supervised-finetuning-sft-base-to-instruct"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/supervised-finetuning-sft-base-to-instruct"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Supervised Fine-Tuning (SFT) anatomi: pre-trained base model → instruction-following model. Instruction dataset (Alpaca, OASST, Dolly), chat template uygulaması, loss masking (sadece response üzerinde loss), hyperparameter farkları (lr 1/10 of pre-train), Llama-3-Instruct production recipe, Türkçe için pratik fine-tune.</image:caption>
      <image:title>Supervised Fine-Tuning (SFT): Pre-trained Base Model&apos;i Instruct&apos;a Dönüştürme — Llama-3-Instruct Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/supervised-finetuning-sft-base-to-instruct</loc>
    <lastmod>2026-05-13T13:00:29.393Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/supervised-finetuning-sft-base-to-instruct"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/supervised-finetuning-sft-base-to-instruct"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/supervised-finetuning-sft-base-to-instruct"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Supervised Fine-Tuning (SFT) anatomi: pre-trained base model → instruction-following model. Instruction dataset (Alpaca, OASST, Dolly), chat template uygulaması, loss masking (sadece response üzerinde loss), hyperparameter farkları (lr 1/10 of pre-train), Llama-3-Instruct production recipe, Türkçe için pratik fine-tune.</image:caption>
      <image:title>Supervised Fine-Tuning (SFT): Pre-trained Base Model&apos;i Instruct&apos;a Dönüştürme — Llama-3-Instruct Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/lora-qlora-parameter-efficient-finetuning</loc>
    <lastmod>2026-05-13T13:00:29.477Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/lora-qlora-parameter-efficient-finetuning"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/lora-qlora-parameter-efficient-finetuning"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/lora-qlora-parameter-efficient-finetuning"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LoRA (Hu 2021): low-rank decomposition fine-tuning — base weights frozen, sadece küçük adapter eğit. %1 parameters, %95+ quality preservation. QLoRA (Dettmers 2023): 4-bit base + LoRA, 70B model&apos;i consumer GPU&apos;da fine-tune. NF4 quantization, paged optimizer. Türkçe pratik: $5K maliyetle production Türkçe Llama-3 70B.</image:caption>
      <image:title>LoRA + QLoRA: Parameter-Efficient Fine-Tuning Devrim — Hu 2021&apos;den Dettmers 2023&apos;e</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/lora-qlora-parameter-efficient-finetuning</loc>
    <lastmod>2026-05-13T13:00:29.477Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/lora-qlora-parameter-efficient-finetuning"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/lora-qlora-parameter-efficient-finetuning"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/lora-qlora-parameter-efficient-finetuning"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LoRA (Hu 2021): low-rank decomposition fine-tuning — base weights frozen, sadece küçük adapter eğit. %1 parameters, %95+ quality preservation. QLoRA (Dettmers 2023): 4-bit base + LoRA, 70B model&apos;i consumer GPU&apos;da fine-tune. NF4 quantization, paged optimizer. Türkçe pratik: $5K maliyetle production Türkçe Llama-3 70B.</image:caption>
      <image:title>LoRA + QLoRA: Parameter-Efficient Fine-Tuning Devrim — Hu 2021&apos;den Dettmers 2023&apos;e</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkish-llama-3-qlora-finetune</loc>
    <lastmod>2026-05-13T13:00:29.575Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkish-llama-3-qlora-finetune"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkish-llama-3-qlora-finetune"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkish-llama-3-qlora-finetune"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 14 capstone: Llama-3-8B base + Türkçe SFT + QLoRA = production-quality Türkçe Llama-3-Instruct. Dataset curation (50K Türkçe instruction), QLoRA training (single H100 8 saat), evaluation (MT-Bench-TR), HuggingFace Hub publish, vLLM inference deployment.</image:caption>
      <image:title>Capstone Modül 14: Türkçe Llama-3 8B Production Fine-Tune — QLoRA + SFT End-to-End</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkish-llama-3-qlora-finetune</loc>
    <lastmod>2026-05-13T13:00:29.575Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkish-llama-3-qlora-finetune"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkish-llama-3-qlora-finetune"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkish-llama-3-qlora-finetune"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 14 capstone: Llama-3-8B base + Türkçe SFT + QLoRA = production-quality Türkçe Llama-3-Instruct. Dataset curation (50K Türkçe instruction), QLoRA training (single H100 8 saat), evaluation (MT-Bench-TR), HuggingFace Hub publish, vLLM inference deployment.</image:caption>
      <image:title>Capstone Modül 14: Türkçe Llama-3 8B Production Fine-Tune — QLoRA + SFT End-to-End</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/rlhf-ouyang-2022-instructgpt-chatgpt</loc>
    <lastmod>2026-05-13T11:17:37.253Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/rlhf-ouyang-2022-instructgpt-chatgpt"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/rlhf-ouyang-2022-instructgpt-chatgpt"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/rlhf-ouyang-2022-instructgpt-chatgpt"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>RLHF&apos;in tam anatomisi: SFT model → reward model training (Bradley-Terry) → PPO RL training. Ouyang 2022 InstructGPT paper, 3-stage pipeline, KL divergence penalty, reward hacking concerns. ChatGPT&apos;nin gizli sosu. Türkçe RLHF zorlukları (human annotator pool, cultural nuances).</image:caption>
      <image:title>RLHF: Reinforcement Learning from Human Feedback — Ouyang 2022 InstructGPT&apos;den ChatGPT&apos;ye</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/rlhf-ouyang-2022-instructgpt-chatgpt</loc>
    <lastmod>2026-05-13T11:17:37.253Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/rlhf-ouyang-2022-instructgpt-chatgpt"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/rlhf-ouyang-2022-instructgpt-chatgpt"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/rlhf-ouyang-2022-instructgpt-chatgpt"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>RLHF&apos;in tam anatomisi: SFT model → reward model training (Bradley-Terry) → PPO RL training. Ouyang 2022 InstructGPT paper, 3-stage pipeline, KL divergence penalty, reward hacking concerns. ChatGPT&apos;nin gizli sosu. Türkçe RLHF zorlukları (human annotator pool, cultural nuances).</image:caption>
      <image:title>RLHF: Reinforcement Learning from Human Feedback — Ouyang 2022 InstructGPT&apos;den ChatGPT&apos;ye</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/dpo-direct-preference-optimization-rafailov-2023</loc>
    <lastmod>2026-05-13T11:17:37.339Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/dpo-direct-preference-optimization-rafailov-2023"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/dpo-direct-preference-optimization-rafailov-2023"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/dpo-direct-preference-optimization-rafailov-2023"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>DPO (Rafailov 2023): RLHF mathematical reformulation — no reward model, no RL. Direct preference loss. Llama-3 RLHF replacement. Math derivation, implementation simpler than PPO, comparable quality. Türkçe DPO pratik: $1K maliyetle 8B model alignment.</image:caption>
      <image:title>DPO: Direct Preference Optimization — Rafailov 2023, RLHF&apos;in Cheaper Yeniden Doğuşu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/dpo-direct-preference-optimization-rafailov-2023</loc>
    <lastmod>2026-05-13T11:17:37.339Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/dpo-direct-preference-optimization-rafailov-2023"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/dpo-direct-preference-optimization-rafailov-2023"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/dpo-direct-preference-optimization-rafailov-2023"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>DPO (Rafailov 2023): RLHF mathematical reformulation — no reward model, no RL. Direct preference loss. Llama-3 RLHF replacement. Math derivation, implementation simpler than PPO, comparable quality. Türkçe DPO pratik: $1K maliyetle 8B model alignment.</image:caption>
      <image:title>DPO: Direct Preference Optimization — Rafailov 2023, RLHF&apos;in Cheaper Yeniden Doğuşu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/vllm-production-serving-deployment</loc>
    <lastmod>2026-05-13T11:47:21.433Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vllm-production-serving-deployment"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/vllm-production-serving-deployment"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vllm-production-serving-deployment"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>vLLM production deployment: paged attention (Kwon 2023), continuous batching, OpenAI-compatible API, multi-GPU tensor parallel serving, Kubernetes deployment patterns. Llama-3-8B + custom Türkçe model serving 1000+ concurrent users.</image:caption>
      <image:title>vLLM Production Serving: Paged Attention + Continuous Batching ile 10x Throughput</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/vllm-production-serving-deployment</loc>
    <lastmod>2026-05-13T11:47:21.433Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vllm-production-serving-deployment"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/vllm-production-serving-deployment"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vllm-production-serving-deployment"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>vLLM production deployment: paged attention (Kwon 2023), continuous batching, OpenAI-compatible API, multi-GPU tensor parallel serving, Kubernetes deployment patterns. Llama-3-8B + custom Türkçe model serving 1000+ concurrent users.</image:caption>
      <image:title>vLLM Production Serving: Paged Attention + Continuous Batching ile 10x Throughput</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/quantization-capstone-turkish-chatgpt-clone</loc>
    <lastmod>2026-05-13T11:47:21.521Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/quantization-capstone-turkish-chatgpt-clone"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/quantization-capstone-turkish-chatgpt-clone"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/quantization-capstone-turkish-chatgpt-clone"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 16 capstone (müfredatın final capstone&apos;u): GPTQ, AWQ, GGUF quantization formats. Türkçe Llama-3-8B-Instruct quantize + vLLM serve + Next.js frontend = Türkçe ChatGPT klonu. sukruyusufkaya.com/ai-asistan production deploy. Müfredatın sentezi, gerçek dünya artefakt.</image:caption>
      <image:title>Quantization (GPTQ/AWQ/GGUF) + Final Capstone: Türkçe ChatGPT Klonu Production&apos;da</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/quantization-capstone-turkish-chatgpt-clone</loc>
    <lastmod>2026-05-13T11:47:21.521Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/quantization-capstone-turkish-chatgpt-clone"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/quantization-capstone-turkish-chatgpt-clone"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/quantization-capstone-turkish-chatgpt-clone"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 16 capstone (müfredatın final capstone&apos;u): GPTQ, AWQ, GGUF quantization formats. Türkçe Llama-3-8B-Instruct quantize + vLLM serve + Next.js frontend = Türkçe ChatGPT klonu. sukruyusufkaya.com/ai-asistan production deploy. Müfredatın sentezi, gerçek dünya artefakt.</image:caption>
      <image:title>Quantization (GPTQ/AWQ/GGUF) + Final Capstone: Türkçe ChatGPT Klonu Production&apos;da</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/reasoning-models-o1-deepseek-r1-test-time-compute</loc>
    <lastmod>2026-05-13T11:59:44.371Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reasoning-models-o1-deepseek-r1-test-time-compute"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/reasoning-models-o1-deepseek-r1-test-time-compute"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reasoning-models-o1-deepseek-r1-test-time-compute"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>2024-2026 LLM frontier&apos;ı: reasoning models. OpenAI o1 (Eylül 2024), DeepSeek-R1 (Ocak 2025) devrim. Test-time compute scaling (Kaplan&apos;ın yeni boyutu), chain-of-thought intensification, hidden reasoning tokens (o1) vs visible (R1), RL training reasoning patterns. AIME, MATH benchmark devrim, GPT-4 → o1 90% accuracy sıçraması.</image:caption>
      <image:title>Reasoning Devrim: OpenAI o1&apos;den DeepSeek-R1&apos;e — Test-Time Compute ve Chain-of-Thought&apos;un Yeniden Doğuşu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/reasoning-models-o1-deepseek-r1-test-time-compute</loc>
    <lastmod>2026-05-13T11:59:44.371Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reasoning-models-o1-deepseek-r1-test-time-compute"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/reasoning-models-o1-deepseek-r1-test-time-compute"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reasoning-models-o1-deepseek-r1-test-time-compute"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>2024-2026 LLM frontier&apos;ı: reasoning models. OpenAI o1 (Eylül 2024), DeepSeek-R1 (Ocak 2025) devrim. Test-time compute scaling (Kaplan&apos;ın yeni boyutu), chain-of-thought intensification, hidden reasoning tokens (o1) vs visible (R1), RL training reasoning patterns. AIME, MATH benchmark devrim, GPT-4 → o1 90% accuracy sıçraması.</image:caption>
      <image:title>Reasoning Devrim: OpenAI o1&apos;den DeepSeek-R1&apos;e — Test-Time Compute ve Chain-of-Thought&apos;un Yeniden Doğuşu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/deepseek-r1-self-host-turkish-reasoning</loc>
    <lastmod>2026-05-13T11:59:44.461Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/deepseek-r1-self-host-turkish-reasoning"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/deepseek-r1-self-host-turkish-reasoning"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/deepseek-r1-self-host-turkish-reasoning"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>DeepSeek-R1-distilled (7B, 14B, 32B) self-host: vLLM deployment, hardware requirements, prompt patterns for reasoning, Türkçe math problem solving demo. Reasoning model production usage: when, how, cost-benefit.</image:caption>
      <image:title>DeepSeek-R1 Self-Host + Türkçe Reasoning: Distilled Models, Prompt Patterns, Production Deployment</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/deepseek-r1-self-host-turkish-reasoning</loc>
    <lastmod>2026-05-13T11:59:44.461Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/deepseek-r1-self-host-turkish-reasoning"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/deepseek-r1-self-host-turkish-reasoning"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/deepseek-r1-self-host-turkish-reasoning"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>DeepSeek-R1-distilled (7B, 14B, 32B) self-host: vLLM deployment, hardware requirements, prompt patterns for reasoning, Türkçe math problem solving demo. Reasoning model production usage: when, how, cost-benefit.</image:caption>
      <image:title>DeepSeek-R1 Self-Host + Türkçe Reasoning: Distilled Models, Prompt Patterns, Production Deployment</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/mixture-of-experts-mixtral-deepseek-v3</loc>
    <lastmod>2026-05-13T12:20:23.587Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/mixture-of-experts-mixtral-deepseek-v3"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/mixture-of-experts-mixtral-deepseek-v3"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/mixture-of-experts-mixtral-deepseek-v3"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Mixture of Experts (MoE) mimarisi: sparse activation, expert routing (top-k gating), Mixtral 8x7B (Ocak 2024) açık-kaynak devrim, DeepSeek-V3 671B (Aralık 2024) frontier. Routing math (Shazeer 2017 outrageously sparse), auxiliary loss, load balancing. Memory-efficient frontier scale modeller.</image:caption>
      <image:title>Mixture of Experts (MoE): Sparse Activation Devrim — Mixtral 8x7B&apos;den DeepSeek-V3&apos;e</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/mixture-of-experts-mixtral-deepseek-v3</loc>
    <lastmod>2026-05-13T12:20:23.587Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/mixture-of-experts-mixtral-deepseek-v3"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/mixture-of-experts-mixtral-deepseek-v3"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/mixture-of-experts-mixtral-deepseek-v3"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Mixture of Experts (MoE) mimarisi: sparse activation, expert routing (top-k gating), Mixtral 8x7B (Ocak 2024) açık-kaynak devrim, DeepSeek-V3 671B (Aralık 2024) frontier. Routing math (Shazeer 2017 outrageously sparse), auxiliary loss, load balancing. Memory-efficient frontier scale modeller.</image:caption>
      <image:title>Mixture of Experts (MoE): Sparse Activation Devrim — Mixtral 8x7B&apos;den DeepSeek-V3&apos;e</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/vision-language-models-clip-gpt-4o-llama-vision</loc>
    <lastmod>2026-05-13T12:28:50.176Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vision-language-models-clip-gpt-4o-llama-vision"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/vision-language-models-clip-gpt-4o-llama-vision"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vision-language-models-clip-gpt-4o-llama-vision"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Vision-Language Models (VLM) anatomi: CLIP (Radford 2021) image-text alignment, image patch embedding (ViT), projection layer LLM&apos;e, GPT-4V (Eylül 2023), GPT-4o (Mayıs 2024) unified, Llama-3.2 Vision (Eylül 2024) open-source. Mimari: image encoder + projection + LLM. Türkçe multimodal pratik.</image:caption>
      <image:title>Vision-Language Models: CLIP&apos;ten GPT-4o&apos;ya — Image Encoder + LLM Birleşimi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/vision-language-models-clip-gpt-4o-llama-vision</loc>
    <lastmod>2026-05-13T12:28:50.176Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vision-language-models-clip-gpt-4o-llama-vision"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/vision-language-models-clip-gpt-4o-llama-vision"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vision-language-models-clip-gpt-4o-llama-vision"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Vision-Language Models (VLM) anatomi: CLIP (Radford 2021) image-text alignment, image patch embedding (ViT), projection layer LLM&apos;e, GPT-4V (Eylül 2023), GPT-4o (Mayıs 2024) unified, Llama-3.2 Vision (Eylül 2024) open-source. Mimari: image encoder + projection + LLM. Türkçe multimodal pratik.</image:caption>
      <image:title>Vision-Language Models: CLIP&apos;ten GPT-4o&apos;ya — Image Encoder + LLM Birleşimi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/tool-use-function-calling-mcp</loc>
    <lastmod>2026-05-13T12:42:29.037Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tool-use-function-calling-mcp"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tool-use-function-calling-mcp"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tool-use-function-calling-mcp"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551434678-e076c223a692?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tool Use anatomi: LLM&apos;in JSON schema tool tanımlarını okuyup, doğru tool ile doğru parametre seçimi. OpenAI function calling (Haziran 2023), Anthropic MCP (Model Context Protocol, Kasım 2024), Llama-3 tool tokens. Production agent patterns: ReAct, Plan-and-Execute, Reflexion. Türkçe agent pratik.</image:caption>
      <image:title>Tool Use + Function Calling: LLM&apos;in Dış Dünyaya Açılan Kapıları — OpenAI Tools&apos;tan MCP&apos;ye</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tool-use-function-calling-mcp</loc>
    <lastmod>2026-05-13T12:42:29.037Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tool-use-function-calling-mcp"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tool-use-function-calling-mcp"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tool-use-function-calling-mcp"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551434678-e076c223a692?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tool Use anatomi: LLM&apos;in JSON schema tool tanımlarını okuyup, doğru tool ile doğru parametre seçimi. OpenAI function calling (Haziran 2023), Anthropic MCP (Model Context Protocol, Kasım 2024), Llama-3 tool tokens. Production agent patterns: ReAct, Plan-and-Execute, Reflexion. Türkçe agent pratik.</image:caption>
      <image:title>Tool Use + Function Calling: LLM&apos;in Dış Dünyaya Açılan Kapıları — OpenAI Tools&apos;tan MCP&apos;ye</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/llm-evaluation-mmlu-helm-mt-bench-lmsys-arena</loc>
    <lastmod>2026-05-13T12:47:42.197Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/llm-evaluation-mmlu-helm-mt-bench-lmsys-arena"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/llm-evaluation-mmlu-helm-mt-bench-lmsys-arena"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/llm-evaluation-mmlu-helm-mt-bench-lmsys-arena"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM evaluation framework&apos;leri: MMLU (Hendrycks 2020) general knowledge, HELM (Stanford 2022) comprehensive, MT-Bench (Zheng 2023) chat, LMSys Chatbot Arena (community ELO ranking), GPQA (Rein 2023) graduate-level, HumanEval/MBPP code. Türkçe benchmarks (TR-MMLU, MUKAYESE). Benchmark contamination concern, holistic evaluation.</image:caption>
      <image:title>LLM Evaluation Benchmark&apos;ları: MMLU, HELM, MT-Bench, LMSys Arena — Quality Ölçümünün Anatomi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/llm-evaluation-mmlu-helm-mt-bench-lmsys-arena</loc>
    <lastmod>2026-05-13T12:47:42.197Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/llm-evaluation-mmlu-helm-mt-bench-lmsys-arena"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/llm-evaluation-mmlu-helm-mt-bench-lmsys-arena"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/llm-evaluation-mmlu-helm-mt-bench-lmsys-arena"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM evaluation framework&apos;leri: MMLU (Hendrycks 2020) general knowledge, HELM (Stanford 2022) comprehensive, MT-Bench (Zheng 2023) chat, LMSys Chatbot Arena (community ELO ranking), GPQA (Rein 2023) graduate-level, HumanEval/MBPP code. Türkçe benchmarks (TR-MMLU, MUKAYESE). Benchmark contamination concern, holistic evaluation.</image:caption>
      <image:title>LLM Evaluation Benchmark&apos;ları: MMLU, HELM, MT-Bench, LMSys Arena — Quality Ölçümünün Anatomi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/ai-safety-jailbreak-red-teaming-constitutional-kvkk</loc>
    <lastmod>2026-05-13T12:55:10.865Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ai-safety-jailbreak-red-teaming-constitutional-kvkk"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/ai-safety-jailbreak-red-teaming-constitutional-kvkk"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ai-safety-jailbreak-red-teaming-constitutional-kvkk"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>AI safety production&apos;da: jailbreak saldırıları + defense, red-teaming protocols, Anthropic Constitutional AI (Bai 2022), OpenAI alignment, Türkçe için KVKK + AB AI Act 2024 uyumluluk. Production deployment&apos;ta safety guardrails, content filtering, audit logs.</image:caption>
      <image:title>AI Safety + Alignment: Jailbreak Defense, Red-Teaming, Constitutional AI, KVKK Uyumluluğu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/ai-safety-jailbreak-red-teaming-constitutional-kvkk</loc>
    <lastmod>2026-05-13T12:55:10.865Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ai-safety-jailbreak-red-teaming-constitutional-kvkk"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/ai-safety-jailbreak-red-teaming-constitutional-kvkk"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ai-safety-jailbreak-red-teaming-constitutional-kvkk"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>AI safety production&apos;da: jailbreak saldırıları + defense, red-teaming protocols, Anthropic Constitutional AI (Bai 2022), OpenAI alignment, Türkçe için KVKK + AB AI Act 2024 uyumluluk. Production deployment&apos;ta safety guardrails, content filtering, audit logs.</image:caption>
      <image:title>AI Safety + Alignment: Jailbreak Defense, Red-Teaming, Constitutional AI, KVKK Uyumluluğu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/rlhf-dogusu-christiano-2017-chatgpt-tarih-felsefe</loc>
    <lastmod>2026-05-13T13:00:29.665Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/rlhf-dogusu-christiano-2017-chatgpt-tarih-felsefe"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/rlhf-dogusu-christiano-2017-chatgpt-tarih-felsefe"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/rlhf-dogusu-christiano-2017-chatgpt-tarih-felsefe"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>RLHF&apos;in tarihsel ve felsefi temelleri: Christiano vd. 2017 &apos;Deep RL from Human Preferences&apos; paper&apos;ından başlayarak, Stiennon 2020 özetleme çalışması, Ouyang 2022 InstructGPT, Aralık 2022 ChatGPT lansmanına uzanan yedi yıllık dönüşüm. Niye sadece SFT yetmiyor, &apos;helpful-harmless-honest&apos; üçgeninin gerilimi, Goodhart Yasası ve reward hacking sorunu. Türkçe için kültürel bağlamla alignment ne demek — sen/siz ayrımı, sosyal hassasiyet, KVKK sınırı. Müfredatın en kritik kavramsal dersi.</image:caption>
      <image:title>RLHF&apos;in Doğuşu: Christiano 2017&apos;den ChatGPT&apos;ye Yedi Yıllık Yolculuk — İnsan Tercihiyle Hizalama&apos;nın Tarihsel ve Felsefi Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/rlhf-dogusu-christiano-2017-chatgpt-tarih-felsefe</loc>
    <lastmod>2026-05-13T13:00:29.665Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/rlhf-dogusu-christiano-2017-chatgpt-tarih-felsefe"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/rlhf-dogusu-christiano-2017-chatgpt-tarih-felsefe"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/rlhf-dogusu-christiano-2017-chatgpt-tarih-felsefe"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>RLHF&apos;in tarihsel ve felsefi temelleri: Christiano vd. 2017 &apos;Deep RL from Human Preferences&apos; paper&apos;ından başlayarak, Stiennon 2020 özetleme çalışması, Ouyang 2022 InstructGPT, Aralık 2022 ChatGPT lansmanına uzanan yedi yıllık dönüşüm. Niye sadece SFT yetmiyor, &apos;helpful-harmless-honest&apos; üçgeninin gerilimi, Goodhart Yasası ve reward hacking sorunu. Türkçe için kültürel bağlamla alignment ne demek — sen/siz ayrımı, sosyal hassasiyet, KVKK sınırı. Müfredatın en kritik kavramsal dersi.</image:caption>
      <image:title>RLHF&apos;in Doğuşu: Christiano 2017&apos;den ChatGPT&apos;ye Yedi Yıllık Yolculuk — İnsan Tercihiyle Hizalama&apos;nın Tarihsel ve Felsefi Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/reward-model-matematik-bradley-terry-modern-mimari</loc>
    <lastmod>2026-05-13T13:00:29.754Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reward-model-matematik-bradley-terry-modern-mimari"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/reward-model-matematik-bradley-terry-modern-mimari"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reward-model-matematik-bradley-terry-modern-mimari"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>RLHF&apos;in kalbi olan reward model&apos;in matematiksel anatomisi: Bradley-Terry 1952 logistik tercih modelinin türetilmesi, sigmoid&apos;in olasılıkçı yorumu, ranking loss&apos;un türevi, RM mimari seçimleri (SFT&apos;den ayrı vs ortak gövde + value head), kalibrasyon ve overconfidence sorunları, multiple comparison&apos;lar için Plackett-Luce uzantısı, Türkçe için RM eğitiminin pratik tuzakları.</image:caption>
      <image:title>Reward Model&apos;in Matematiği: Bradley-Terry 1952&apos;den Modern LLM Reward Mimari&apos;ye — Tercihten Skalar Skora Geçiş</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/reward-model-matematik-bradley-terry-modern-mimari</loc>
    <lastmod>2026-05-13T13:00:29.754Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reward-model-matematik-bradley-terry-modern-mimari"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/reward-model-matematik-bradley-terry-modern-mimari"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reward-model-matematik-bradley-terry-modern-mimari"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>RLHF&apos;in kalbi olan reward model&apos;in matematiksel anatomisi: Bradley-Terry 1952 logistik tercih modelinin türetilmesi, sigmoid&apos;in olasılıkçı yorumu, ranking loss&apos;un türevi, RM mimari seçimleri (SFT&apos;den ayrı vs ortak gövde + value head), kalibrasyon ve overconfidence sorunları, multiple comparison&apos;lar için Plackett-Luce uzantısı, Türkçe için RM eğitiminin pratik tuzakları.</image:caption>
      <image:title>Reward Model&apos;in Matematiği: Bradley-Terry 1952&apos;den Modern LLM Reward Mimari&apos;ye — Tercihten Skalar Skora Geçiş</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/ppo-algoritma-satir-satir-schulman-2017</loc>
    <lastmod>2026-05-13T13:00:29.841Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ppo-algoritma-satir-satir-schulman-2017"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/ppo-algoritma-satir-satir-schulman-2017"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ppo-algoritma-satir-satir-schulman-2017"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Proximal Policy Optimization (Schulman 2017) algoritmasının LLM RLHF&apos;e uyarlanması: policy gradient temeli, advantage estimation (GAE), clipped surrogate loss&apos;un türevi ve neden &apos;clip&apos;, KL penalty matematiği, value function loss, entropi bonusu. InstructGPT&apos;nin tam PPO setup&apos;ı, hyperparametre seçimleri, eğitim stabilitesi, debug stratejileri.</image:caption>
      <image:title>PPO Algoritması Satır Satır: Schulman 2017&apos;den InstructGPT&apos;ye — RL&apos;in LLM&apos;e Uyarlanması</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/ppo-algoritma-satir-satir-schulman-2017</loc>
    <lastmod>2026-05-13T13:00:29.841Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ppo-algoritma-satir-satir-schulman-2017"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/ppo-algoritma-satir-satir-schulman-2017"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/ppo-algoritma-satir-satir-schulman-2017"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Proximal Policy Optimization (Schulman 2017) algoritmasının LLM RLHF&apos;e uyarlanması: policy gradient temeli, advantage estimation (GAE), clipped surrogate loss&apos;un türevi ve neden &apos;clip&apos;, KL penalty matematiği, value function loss, entropi bonusu. InstructGPT&apos;nin tam PPO setup&apos;ı, hyperparametre seçimleri, eğitim stabilitesi, debug stratejileri.</image:caption>
      <image:title>PPO Algoritması Satır Satır: Schulman 2017&apos;den InstructGPT&apos;ye — RL&apos;in LLM&apos;e Uyarlanması</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/dpo-devrim-rafailov-2023-matematik-kesfi</loc>
    <lastmod>2026-05-13T13:00:29.930Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/dpo-devrim-rafailov-2023-matematik-kesfi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/dpo-devrim-rafailov-2023-matematik-kesfi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/dpo-devrim-rafailov-2023-matematik-kesfi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Direct Preference Optimization (Rafailov vd. 2023): RLHF&apos;in 3 aşamasını tek supervised loss&apos;a indiren matematik keşfin tam türevi. Reward model&apos;in &apos;gizli reformülasyonu&apos;, Bradley-Terry + KL constraint optimum çözümü, neden DPO &apos;her LLM zaten reward model&apos; diyor, kapalı form çözümün matematik anlamı. PPO ile sayısal karşılaştırma, modern DPO varyantları (IPO, KTO, SimPO), Türkçe DPO production pipeline&apos;ı.</image:caption>
      <image:title>DPO Devrim: Rafailov 2023&apos;ün Matematik Keşfi — RLHF&apos;i Tek Loss Fonksiyonuna Sıkıştırmak</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/dpo-devrim-rafailov-2023-matematik-kesfi</loc>
    <lastmod>2026-05-13T13:00:29.930Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/dpo-devrim-rafailov-2023-matematik-kesfi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/dpo-devrim-rafailov-2023-matematik-kesfi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/dpo-devrim-rafailov-2023-matematik-kesfi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Direct Preference Optimization (Rafailov vd. 2023): RLHF&apos;in 3 aşamasını tek supervised loss&apos;a indiren matematik keşfin tam türevi. Reward model&apos;in &apos;gizli reformülasyonu&apos;, Bradley-Terry + KL constraint optimum çözümü, neden DPO &apos;her LLM zaten reward model&apos; diyor, kapalı form çözümün matematik anlamı. PPO ile sayısal karşılaştırma, modern DPO varyantları (IPO, KTO, SimPO), Türkçe DPO production pipeline&apos;ı.</image:caption>
      <image:title>DPO Devrim: Rafailov 2023&apos;ün Matematik Keşfi — RLHF&apos;i Tek Loss Fonksiyonuna Sıkıştırmak</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/grpo-reasoning-rl-deepseek-r1</loc>
    <lastmod>2026-05-13T13:00:30.020Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/grpo-reasoning-rl-deepseek-r1"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/grpo-reasoning-rl-deepseek-r1"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/grpo-reasoning-rl-deepseek-r1"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GRPO (Group Relative Policy Optimization): DeepSeek&apos;in PPO&apos;ya getirdiği elegant sadeleştirme. Value function olmadan advantage tahmini, grup karşılaştırması, computational verimlilik. DeepSeek-R1 paper&apos;ının (Ocak 2025) anatomi, reasoning eğitiminin RL sıralaması, &apos;aha moments&apos; fenomeni, process reward model&apos;lerin rolü, o1 vs R1 mimari karşılaştırma, Türkçe reasoning model&apos;i için pratik notlar.</image:caption>
      <image:title>GRPO ve Reasoning RL: DeepSeek-R1&apos;in İçi — Grup-Bazlı Avantaj Tahmininden Process Reward&apos;a</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/grpo-reasoning-rl-deepseek-r1</loc>
    <lastmod>2026-05-13T13:00:30.020Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/grpo-reasoning-rl-deepseek-r1"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/grpo-reasoning-rl-deepseek-r1"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/grpo-reasoning-rl-deepseek-r1"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>GRPO (Group Relative Policy Optimization): DeepSeek&apos;in PPO&apos;ya getirdiği elegant sadeleştirme. Value function olmadan advantage tahmini, grup karşılaştırması, computational verimlilik. DeepSeek-R1 paper&apos;ının (Ocak 2025) anatomi, reasoning eğitiminin RL sıralaması, &apos;aha moments&apos; fenomeni, process reward model&apos;lerin rolü, o1 vs R1 mimari karşılaştırma, Türkçe reasoning model&apos;i için pratik notlar.</image:caption>
      <image:title>GRPO ve Reasoning RL: DeepSeek-R1&apos;in İçi — Grup-Bazlı Avantaj Tahmininden Process Reward&apos;a</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-dpo-modeli-uretim</loc>
    <lastmod>2026-05-13T13:00:30.110Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-dpo-modeli-uretim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-dpo-modeli-uretim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-dpo-modeli-uretim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 15 capstone projesi: Llama-3-8B-Instruct üzerine Türkçe DPO ile production-grade model üretmek. 5K Türkçe karşılaştırma verisinin nasıl toplanır (manual + synthetic), DPO eğitimi (QLoRA, single H100, $50), MT-Bench-TR ile değerlendirme, win-rate ölçümü, HuggingFace Hub&apos;da model card ile yayın. Müfredatın altıncı production artefaktı.</image:caption>
      <image:title>Capstone Modül 15: Türkçe DPO Modeli Sıfırdan Üretime — Veri, Eğitim, Değerlendirme, Yayın</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-dpo-modeli-uretim</loc>
    <lastmod>2026-05-13T13:00:30.110Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-dpo-modeli-uretim"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-dpo-modeli-uretim"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-dpo-modeli-uretim"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 15 capstone projesi: Llama-3-8B-Instruct üzerine Türkçe DPO ile production-grade model üretmek. 5K Türkçe karşılaştırma verisinin nasıl toplanır (manual + synthetic), DPO eğitimi (QLoRA, single H100, $50), MT-Bench-TR ile değerlendirme, win-rate ölçümü, HuggingFace Hub&apos;da model card ile yayın. Müfredatın altıncı production artefaktı.</image:caption>
      <image:title>Capstone Modül 15: Türkçe DPO Modeli Sıfırdan Üretime — Veri, Eğitim, Değerlendirme, Yayın</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/self-host-karar-cercevesi-api-vs-gpu</loc>
    <lastmod>2026-05-13T13:00:30.197Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/self-host-karar-cercevesi-api-vs-gpu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/self-host-karar-cercevesi-api-vs-gpu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/self-host-karar-cercevesi-api-vs-gpu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581090464777-f3220bbe1b8b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM üretimine geçişin ilk kritik kararı: API mı, self-host mu? Bu dersin hedefi karar mühendisliğini sağlam temellendirmek. Maliyet matematiği (per-token ekonomisi, fixed vs variable costs), gizlilik (KVKK, sektörel kısıtlar), performans (latency, throughput), bağımsızlık (lock-in riski). Türkçe SaaS için 5 farklı senaryo: chatbot, RAG, content gen, hukuki, sağlık. Her birinde doğru karar farklı.</image:caption>
      <image:title>Self-Host Karar Çerçevesi: OpenAI API vs Kendi GPU&apos;n — Maliyet, Gizlilik, Performans, Bağımsızlık</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/self-host-karar-cercevesi-api-vs-gpu</loc>
    <lastmod>2026-05-13T13:00:30.197Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/self-host-karar-cercevesi-api-vs-gpu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/self-host-karar-cercevesi-api-vs-gpu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/self-host-karar-cercevesi-api-vs-gpu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581090464777-f3220bbe1b8b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM üretimine geçişin ilk kritik kararı: API mı, self-host mu? Bu dersin hedefi karar mühendisliğini sağlam temellendirmek. Maliyet matematiği (per-token ekonomisi, fixed vs variable costs), gizlilik (KVKK, sektörel kısıtlar), performans (latency, throughput), bağımsızlık (lock-in riski). Türkçe SaaS için 5 farklı senaryo: chatbot, RAG, content gen, hukuki, sağlık. Her birinde doğru karar farklı.</image:caption>
      <image:title>Self-Host Karar Çerçevesi: OpenAI API vs Kendi GPU&apos;n — Maliyet, Gizlilik, Performans, Bağımsızlık</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/vllm-production-muhendisligi-paged-attention-sla</loc>
    <lastmod>2026-05-13T13:00:30.285Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vllm-production-muhendisligi-paged-attention-sla"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/vllm-production-muhendisligi-paged-attention-sla"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vllm-production-muhendisligi-paged-attention-sla"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>vLLM&apos;in matematiksel ve sistemsel anatomi: Paged attention (Kwon vd. 2023) niye RAM&apos;i 5× verimli kullanıyor, continuous batching matematik, KV cache&apos;in iç yapısı, OpenAI-uyumlu API, Türkçe Llama-3 deployment&apos;ı baştan sona. Hardware seçimi (H100 vs A100 vs RTX 4090), Kubernetes setup, autoscaling, SLA garantileri.</image:caption>
      <image:title>vLLM Production Mühendisliği: Paged Attention&apos;dan SLA&apos;lara — Modern LLM Sunumunun Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/vllm-production-muhendisligi-paged-attention-sla</loc>
    <lastmod>2026-05-13T13:00:30.285Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vllm-production-muhendisligi-paged-attention-sla"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/vllm-production-muhendisligi-paged-attention-sla"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/vllm-production-muhendisligi-paged-attention-sla"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>vLLM&apos;in matematiksel ve sistemsel anatomi: Paged attention (Kwon vd. 2023) niye RAM&apos;i 5× verimli kullanıyor, continuous batching matematik, KV cache&apos;in iç yapısı, OpenAI-uyumlu API, Türkçe Llama-3 deployment&apos;ı baştan sona. Hardware seçimi (H100 vs A100 vs RTX 4090), Kubernetes setup, autoscaling, SLA garantileri.</image:caption>
      <image:title>vLLM Production Mühendisliği: Paged Attention&apos;dan SLA&apos;lara — Modern LLM Sunumunun Anatomisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/quantization-derinlemesine-int4-fp8</loc>
    <lastmod>2026-05-13T13:00:30.375Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/quantization-derinlemesine-int4-fp8"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/quantization-derinlemesine-int4-fp8"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/quantization-derinlemesine-int4-fp8"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM quantization&apos;ın matematiksel ve mühendislik anatomi: INT8, INT4, FP8 formatları, GPTQ (Frantar 2022) vs AWQ (Lin 2023) vs GGUF (Gerganov) algoritmaları, kalite-boyut-hız trade-off&apos;ları. Llama-3-8B Türkçe DPO model&apos;ini 4-bit AWQ ile quantize etme, kalite kaybı ölçümü, RTX 4090&apos;da Llama-3-70B çalıştırma, mobil cihaz deployment&apos;ı.</image:caption>
      <image:title>Quantization Derinlemesine: INT4&apos;ten FP8&apos;e — Modelinizi 4× Küçültmek, 2× Hızlandırmak</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/quantization-derinlemesine-int4-fp8</loc>
    <lastmod>2026-05-13T13:00:30.375Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/quantization-derinlemesine-int4-fp8"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/quantization-derinlemesine-int4-fp8"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/quantization-derinlemesine-int4-fp8"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM quantization&apos;ın matematiksel ve mühendislik anatomi: INT8, INT4, FP8 formatları, GPTQ (Frantar 2022) vs AWQ (Lin 2023) vs GGUF (Gerganov) algoritmaları, kalite-boyut-hız trade-off&apos;ları. Llama-3-8B Türkçe DPO model&apos;ini 4-bit AWQ ile quantize etme, kalite kaybı ölçümü, RTX 4090&apos;da Llama-3-70B çalıştırma, mobil cihaz deployment&apos;ı.</image:caption>
      <image:title>Quantization Derinlemesine: INT4&apos;ten FP8&apos;e — Modelinizi 4× Küçültmek, 2× Hızlandırmak</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/monitoring-observability-alerting-llm</loc>
    <lastmod>2026-05-13T13:00:30.463Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/monitoring-observability-alerting-llm"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/monitoring-observability-alerting-llm"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/monitoring-observability-alerting-llm"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Production LLM sunumunun izleme ve gözlemlenebilirlik katmanı: Prometheus metrikleri (vLLM native), Grafana dashboard tasarımı, OpenTelemetry tracing, log aggregation (Loki/Elastic), alerting kuralları (Slack/PagerDuty), Sentry ile error tracking. Türkçe-spesifik anomaliler: hallucination tespit, tokenizer hataları, prompt injection alarm. Bir LLM mühendisinin &apos;ne izlemeli&apos; rehberi.</image:caption>
      <image:title>Monitoring, Observability ve Alerting: Production LLM&apos;inizi Gözleyin — Metrikten Eyleme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/monitoring-observability-alerting-llm</loc>
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    <priority>0.60</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/monitoring-observability-alerting-llm"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/monitoring-observability-alerting-llm"/>
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      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Production LLM sunumunun izleme ve gözlemlenebilirlik katmanı: Prometheus metrikleri (vLLM native), Grafana dashboard tasarımı, OpenTelemetry tracing, log aggregation (Loki/Elastic), alerting kuralları (Slack/PagerDuty), Sentry ile error tracking. Türkçe-spesifik anomaliler: hallucination tespit, tokenizer hataları, prompt injection alarm. Bir LLM mühendisinin &apos;ne izlemeli&apos; rehberi.</image:caption>
      <image:title>Monitoring, Observability ve Alerting: Production LLM&apos;inizi Gözleyin — Metrikten Eyleme</image:title>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-chatgpt-klonu-yayinda</loc>
    <lastmod>2026-05-13T13:00:30.548Z</lastmod>
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    <priority>0.70</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-chatgpt-klonu-yayinda"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-chatgpt-klonu-yayinda"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 16&apos;nın capstone&apos;u: 4 dersin (karar, vLLM, quantization, monitoring) sentezini gerçek bir ürüne dönüştürmek. Modül 15.6&apos;daki Türkçe DPO modelimizi → 4-bit AWQ quantize → vLLM serve → Next.js frontend + streaming → Vercel deploy → Sentry + Grafana monitoring → **chat.sukruyusufkaya.com**&apos;da yayında. Müfredatın 7. production artefaktı. Backend ($60/ay maliyet), frontend (Vercel free tier), monitoring (Grafana Cloud free) ile tam stack.</image:caption>
      <image:title>Capstone Modül 16: Türkçe ChatGPT Klonu Yayında — Modülün 16 Bütünleştirilmesi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-chatgpt-klonu-yayinda</loc>
    <lastmod>2026-05-13T13:00:30.548Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-chatgpt-klonu-yayinda"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-chatgpt-klonu-yayinda"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 16&apos;nın capstone&apos;u: 4 dersin (karar, vLLM, quantization, monitoring) sentezini gerçek bir ürüne dönüştürmek. Modül 15.6&apos;daki Türkçe DPO modelimizi → 4-bit AWQ quantize → vLLM serve → Next.js frontend + streaming → Vercel deploy → Sentry + Grafana monitoring → **chat.sukruyusufkaya.com**&apos;da yayında. Müfredatın 7. production artefaktı. Backend ($60/ay maliyet), frontend (Vercel free tier), monitoring (Grafana Cloud free) ile tam stack.</image:caption>
      <image:title>Capstone Modül 16: Türkçe ChatGPT Klonu Yayında — Modülün 16 Bütünleştirilmesi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/reasoning-tarih-chain-of-thought-o1-dogus</loc>
    <lastmod>2026-05-13T13:00:30.635Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reasoning-tarih-chain-of-thought-o1-dogus"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/reasoning-tarih-chain-of-thought-o1-dogus"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reasoning-tarih-chain-of-thought-o1-dogus"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Reasoning model&apos;lerin tarihsel ve kavramsal anatomi: Wei vd. 2022 &apos;Chain-of-Thought Prompting&apos;ten 12 Eylül 2024 OpenAI o1 lansmanına yedi yıl. Self-consistency (Wang 2022), Tree of Thoughts (Yao 2023), Reflexion (Shinn 2023) — prompting-based reasoning&apos;in yükselişi ve sınırları. Niye 2024&apos;e kadar &apos;reasoning model&apos; yoktu, niye o1 farklıydı, test-time compute&apos;un yeni scaling boyutu olarak ortaya çıkışı. Türkçe matematik problemi çözen modeller için ne ifade ediyor.</image:caption>
      <image:title>Reasoning Devrimi&apos;nin Tarihi: Wei 2022 Chain-of-Thought&apos;tan o1&apos;e — &apos;Düşünmeyi Öğrenen Modellerin&apos; Yedi Yıllık Doğuşu</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/reasoning-tarih-chain-of-thought-o1-dogus</loc>
    <lastmod>2026-05-13T13:00:30.635Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reasoning-tarih-chain-of-thought-o1-dogus"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/reasoning-tarih-chain-of-thought-o1-dogus"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/reasoning-tarih-chain-of-thought-o1-dogus"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Reasoning model&apos;lerin tarihsel ve kavramsal anatomi: Wei vd. 2022 &apos;Chain-of-Thought Prompting&apos;ten 12 Eylül 2024 OpenAI o1 lansmanına yedi yıl. Self-consistency (Wang 2022), Tree of Thoughts (Yao 2023), Reflexion (Shinn 2023) — prompting-based reasoning&apos;in yükselişi ve sınırları. Niye 2024&apos;e kadar &apos;reasoning model&apos; yoktu, niye o1 farklıydı, test-time compute&apos;un yeni scaling boyutu olarak ortaya çıkışı. Türkçe matematik problemi çözen modeller için ne ifade ediyor.</image:caption>
      <image:title>Reasoning Devrimi&apos;nin Tarihi: Wei 2022 Chain-of-Thought&apos;tan o1&apos;e — &apos;Düşünmeyi Öğrenen Modellerin&apos; Yedi Yıllık Doğuşu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/yapay-zekaya-giris/turkce-icin-yapay-zeka-derinlemesine</loc>
    <lastmod>2026-05-13T12:09:21.920Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/turkce-icin-yapay-zeka-derinlemesine"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/turkce-icin-yapay-zeka-derinlemesine"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/turkce-icin-yapay-zeka-derinlemesine"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&amp;q=80</image:loc>
      <image:caption>Türkçe NLP&apos;nin spesifik zorlukları (aglutinatif morfoloji, ünlü uyumu, tokenization patlaması), Türkçe-özel açık LLM ekosistemi (TURNA, Kanarya, Kumru, Trendyol-LLM), Türkçe RAG kurma rehberi, ve Türkiye AI ekosisteminde nasıl kariyer kuracağınız üzerine kapsamlı bir bölüm. Bu ders Türkçe için bir AI sistemi inşa edecek herkes için zorunlu okuma.</image:caption>
      <image:title>Türkçe için Yapay Zeka Derinlemesine: NLP, LLM ve Pratik Pipeline</image:title>
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  </url>
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    <loc>https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/turkce-icin-yapay-zeka-derinlemesine</loc>
    <lastmod>2026-05-13T12:09:21.920Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/turkce-icin-yapay-zeka-derinlemesine"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/turkce-icin-yapay-zeka-derinlemesine"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/turkce-icin-yapay-zeka-derinlemesine"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&amp;q=80</image:loc>
      <image:caption>Türkçe NLP&apos;nin spesifik zorlukları (aglutinatif morfoloji, ünlü uyumu, tokenization patlaması), Türkçe-özel açık LLM ekosistemi (TURNA, Kanarya, Kumru, Trendyol-LLM), Türkçe RAG kurma rehberi, ve Türkiye AI ekosisteminde nasıl kariyer kuracağınız üzerine kapsamlı bir bölüm. Bu ders Türkçe için bir AI sistemi inşa edecek herkes için zorunlu okuma.</image:caption>
      <image:title>Türkçe için Yapay Zeka Derinlemesine: NLP, LLM ve Pratik Pipeline</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-guvenligi-derinlemesine</loc>
    <lastmod>2026-05-13T12:49:39.417Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-guvenligi-derinlemesine"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/ai-guvenligi-derinlemesine"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-guvenligi-derinlemesine"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&amp;q=80</image:loc>
      <image:caption>Modern AI sistemlerinde saldırı yüzeyleri, NIST AI 100-2 taksonomisi, jailbreaking teknikleri (8+ varyant), prompt injection (direct + indirect), adversarial examples, model stealing, privacy attacks, supply chain saldırıları, agentic AI&apos;nın yeni güvenlik zorlukları ve production red teaming pratikleri. Bu ders her AI mühendisinin bilmesi gerekenleri kapsar.</image:caption>
      <image:title>AI Güvenliği Derinlemesine: Saldırılar, Savunmalar ve Red Teaming</image:title>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/ai-guvenligi-derinlemesine</loc>
    <lastmod>2026-05-13T12:49:39.417Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/ai-guvenligi-derinlemesine"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-guvenligi-derinlemesine"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&amp;q=80</image:loc>
      <image:caption>Modern AI sistemlerinde saldırı yüzeyleri, NIST AI 100-2 taksonomisi, jailbreaking teknikleri (8+ varyant), prompt injection (direct + indirect), adversarial examples, model stealing, privacy attacks, supply chain saldırıları, agentic AI&apos;nın yeni güvenlik zorlukları ve production red teaming pratikleri. Bu ders her AI mühendisinin bilmesi gerekenleri kapsar.</image:caption>
      <image:title>AI Güvenliği Derinlemesine: Saldırılar, Savunmalar ve Red Teaming</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-glossary-hizli-referans</loc>
    <lastmod>2026-05-13T13:01:26.896Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-glossary-hizli-referans"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/ai-glossary-hizli-referans"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-glossary-hizli-referans"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&amp;q=80</image:loc>
      <image:caption>Yapay zeka, makine öğrenmesi, derin öğrenme, LLM, agent, güvenlik ve Türkçe AI ekosistemine ait 120+ temel terim, Türkçe karşılıkları ve kısa tanımlarla. Bu kursu bitirdikten sonra bir başvuru kılavuzu olarak kullanın; yeni bir makale/blog okurken yanınızda bulunsun.</image:caption>
      <image:title>AI Glossary &amp; Hızlı Referans Kılavuzu — 120+ Terim, Türkçe-İngilizce</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/ai-glossary-hizli-referans</loc>
    <lastmod>2026-05-13T13:01:26.896Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-glossary-hizli-referans"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/ai-glossary-hizli-referans"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-glossary-hizli-referans"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&amp;q=80</image:loc>
      <image:caption>Yapay zeka, makine öğrenmesi, derin öğrenme, LLM, agent, güvenlik ve Türkçe AI ekosistemine ait 120+ temel terim, Türkçe karşılıkları ve kısa tanımlarla. Bu kursu bitirdikten sonra bir başvuru kılavuzu olarak kullanın; yeni bir makale/blog okurken yanınızda bulunsun.</image:caption>
      <image:title>AI Glossary &amp; Hızlı Referans Kılavuzu — 120+ Terim, Türkçe-İngilizce</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/veri-merkezli-ai-manifestosu</loc>
    <lastmod>2026-05-13T12:53:31.841Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/veri-merkezli-ai-manifestosu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/veri-merkezli-ai-manifestosu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/veri-merkezli-ai-manifestosu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Andrew Ng&apos;in 80/20 kuralı, modern LLM çağında veri kalitesinin model boyutundan daha kritik olmasının nedenleri, Tesla/OpenAI/Meta&apos;nın veri stratejileri ve neden veri etiketleme bir mühendislik disiplinidir.</image:caption>
      <image:title>Veri-Merkezli AI Manifestosu: Neden Modelden Çok Veriye Yatırım Yapmalısın?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/veri-merkezli-ai-manifestosu</loc>
    <lastmod>2026-05-13T12:53:31.841Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/veri-merkezli-ai-manifestosu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/veri-merkezli-ai-manifestosu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/veri-merkezli-ai-manifestosu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Andrew Ng&apos;in 80/20 kuralı, modern LLM çağında veri kalitesinin model boyutundan daha kritik olmasının nedenleri, Tesla/OpenAI/Meta&apos;nın veri stratejileri ve neden veri etiketleme bir mühendislik disiplinidir.</image:caption>
      <image:title>Veri-Merkezli AI Manifestosu: Neden Modelden Çok Veriye Yatırım Yapmalısın?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/etiketleme-muhendisi-kariyer-haritasi</loc>
    <lastmod>2026-05-13T12:53:31.936Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/etiketleme-muhendisi-kariyer-haritasi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/etiketleme-muhendisi-kariyer-haritasi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/etiketleme-muhendisi-kariyer-haritasi"/>
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      <image:loc>https://images.unsplash.com/photo-1551836022-deb4988cc6c0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Veri etiketleme alanındaki kariyer seviyeleri, günlük iş akışı, yetkinlik matrisi, küresel ve Türkiye maaş aralıkları, kariyer pivotları ve hangi yetenekleri hangi sırayla geliştirmen gerektiği.</image:caption>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/etiketleme-muhendisi-kariyer-haritasi</loc>
    <lastmod>2026-05-13T12:53:31.936Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/etiketleme-muhendisi-kariyer-haritasi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/etiketleme-muhendisi-kariyer-haritasi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/etiketleme-muhendisi-kariyer-haritasi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551836022-deb4988cc6c0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Veri etiketleme alanındaki kariyer seviyeleri, günlük iş akışı, yetkinlik matrisi, küresel ve Türkiye maaş aralıkları, kariyer pivotları ve hangi yetenekleri hangi sırayla geliştirmen gerektiği.</image:caption>
      <image:title>Etiketleme Mühendisinin Kariyer Haritası: Annotator&apos;dan Head of Data Operations&apos;a</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/turkiye-veri-etiketleme-ekosistemi</loc>
    <lastmod>2026-05-13T12:53:32.037Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/turkiye-veri-etiketleme-ekosistemi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/turkiye-veri-etiketleme-ekosistemi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/turkiye-veri-etiketleme-ekosistemi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551836022-deb4988cc6c0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Türkiye&apos;deki veri etiketleme vendor&apos;ları, freelance pazarı, ücret bantları, KVKK&apos;nın yarattığı yerli avantaj, Türkçe veri kıtlığı sorunu ve bunun fırsata nasıl dönüştürüleceği.</image:caption>
      <image:title>Türkiye&apos;deki Veri Etiketleme Ekosistemi: Vendor&apos;lar, Freelance Pazarı, KVKK ve Türkçe Veri Kıtlığı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/turkiye-veri-etiketleme-ekosistemi</loc>
    <lastmod>2026-05-13T12:53:32.037Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/turkiye-veri-etiketleme-ekosistemi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/turkiye-veri-etiketleme-ekosistemi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/turkiye-veri-etiketleme-ekosistemi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551836022-deb4988cc6c0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Türkiye&apos;deki veri etiketleme vendor&apos;ları, freelance pazarı, ücret bantları, KVKK&apos;nın yarattığı yerli avantaj, Türkçe veri kıtlığı sorunu ve bunun fırsata nasıl dönüştürüleceği.</image:caption>
      <image:title>Türkiye&apos;deki Veri Etiketleme Ekosistemi: Vendor&apos;lar, Freelance Pazarı, KVKK ve Türkçe Veri Kıtlığı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/gelistirme-ortami-kurulumu-label-studio-docker</loc>
    <lastmod>2026-05-13T12:53:32.131Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/gelistirme-ortami-kurulumu-label-studio-docker"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/gelistirme-ortami-kurulumu-label-studio-docker"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/gelistirme-ortami-kurulumu-label-studio-docker"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Veri etiketleme &amp; kalite yönetimi kursunun tüm derslerinde kullanacağımız geliştirme ortamı: Python 3.12 (uv ile), Docker Compose, PostgreSQL, Label Studio ve ilk &quot;Hello World&quot; annotation projesi.</image:caption>
      <image:title>[ATÖLYE] Geliştirme Ortamı Kurulumu: Python, Docker, PostgreSQL ve Label Studio&apos;yu Sıfırdan Kuralım</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/gelistirme-ortami-kurulumu-label-studio-docker</loc>
    <lastmod>2026-05-13T12:53:32.131Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/gelistirme-ortami-kurulumu-label-studio-docker"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/gelistirme-ortami-kurulumu-label-studio-docker"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/gelistirme-ortami-kurulumu-label-studio-docker"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1517694712202-14dd9538aa97?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Veri etiketleme &amp; kalite yönetimi kursunun tüm derslerinde kullanacağımız geliştirme ortamı: Python 3.12 (uv ile), Docker Compose, PostgreSQL, Label Studio ve ilk &quot;Hello World&quot; annotation projesi.</image:caption>
      <image:title>[ATÖLYE] Geliştirme Ortamı Kurulumu: Python, Docker, PostgreSQL ve Label Studio&apos;yu Sıfırdan Kuralım</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-engineer-kimdir</loc>
    <lastmod>2026-05-13T13:04:47.735Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-engineer-kimdir"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomaly-detection-engineer-kimdir"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-engineer-kimdir"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anomaly Detection Engineer rolünün ML Engineer, Fraud Analyst, SRE, Quality Engineer ile farkları; yetkinlik matrisi, kıdem seviyeleri, Türkiye ve global maaş aralıkları, günlük iş akışı, sektör beklentileri.</image:caption>
      <image:title>Anomaly Detection Engineer Kimdir? Fraud, SRE, Quality Engineer ile Farklar ve Türkiye Maaş Manzarası</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomaly-detection-engineer-kimdir</loc>
    <lastmod>2026-05-13T13:04:47.735Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-engineer-kimdir"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomaly-detection-engineer-kimdir"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-engineer-kimdir"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anomaly Detection Engineer rolünün ML Engineer, Fraud Analyst, SRE, Quality Engineer ile farkları; yetkinlik matrisi, kıdem seviyeleri, Türkiye ve global maaş aralıkları, günlük iş akışı, sektör beklentileri.</image:caption>
      <image:title>Anomaly Detection Engineer Kimdir? Fraud, SRE, Quality Engineer ile Farklar ve Türkiye Maaş Manzarası</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-kurs-felsefesi</loc>
    <lastmod>2026-05-13T13:04:47.836Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-kurs-felsefesi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomaly-detection-kurs-felsefesi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-kurs-felsefesi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1497032628192-86f99bcd76bc?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>İstatistik → klasik ML → deep learning → time series → domain → production sıralamasını neden seçtik; öğrenme nehri modeli, hangi capstone&apos;la hangi yetenek inşa ediliyor, kursta hangi 5 prensibi takip edeceğiz.</image:caption>
      <image:title>Kurs Felsefesi: Neden Bu Yol, Neden Bu Sıra — Anomaly Detection Öğrenme Nehri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomaly-detection-kurs-felsefesi</loc>
    <lastmod>2026-05-13T13:04:47.836Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-kurs-felsefesi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomaly-detection-kurs-felsefesi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-kurs-felsefesi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1497032628192-86f99bcd76bc?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>İstatistik → klasik ML → deep learning → time series → domain → production sıralamasını neden seçtik; öğrenme nehri modeli, hangi capstone&apos;la hangi yetenek inşa ediliyor, kursta hangi 5 prensibi takip edeceğiz.</image:caption>
      <image:title>Kurs Felsefesi: Neden Bu Yol, Neden Bu Sıra — Anomaly Detection Öğrenme Nehri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-atolye-kurulumu</loc>
    <lastmod>2026-05-13T13:04:47.932Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-atolye-kurulumu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomaly-detection-atolye-kurulumu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-atolye-kurulumu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anomaly detection için uv ile Python 3.12 sanal ortam, PyTorch 2.5+, PyOD, anomalib, alibi-detect, river, Jupyter Lab kurulumu; Windows WSL2, macOS MPS ve Linux CUDA için adım adım rehber.</image:caption>
      <image:title>Atölye Kurulumu: uv + Python 3.12 + PyOD + anomalib + PyTorch — Sıfırdan Production-Ready Anomaly Detection Ortamı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomaly-detection-atolye-kurulumu</loc>
    <lastmod>2026-05-13T13:04:47.932Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-atolye-kurulumu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomaly-detection-atolye-kurulumu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-atolye-kurulumu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anomaly detection için uv ile Python 3.12 sanal ortam, PyTorch 2.5+, PyOD, anomalib, alibi-detect, river, Jupyter Lab kurulumu; Windows WSL2, macOS MPS ve Linux CUDA için adım adım rehber.</image:caption>
      <image:title>Atölye Kurulumu: uv + Python 3.12 + PyOD + anomalib + PyTorch — Sıfırdan Production-Ready Anomaly Detection Ortamı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-veri-hesaplari-cloud</loc>
    <lastmod>2026-05-13T13:04:48.024Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-veri-hesaplari-cloud"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomaly-detection-veri-hesaplari-cloud"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-veri-hesaplari-cloud"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Kursta kullanacağımız 18 veri kümesinin nereden indirileceği, Kaggle API kurulumu, HuggingFace datasets, Numenta NAB, MVTec AD, NASA Turbofan, CWRU bearing, IEEE-CIS Fraud — ve Google Colab/RunPod ile ücretsiz GPU erişimi.</image:caption>
      <image:title>Veri Hesapları &amp; Cloud: Kaggle, HuggingFace, Numenta, MVTec, NASA — Anomaly Detection Veri Cephaneliği</image:title>
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  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomaly-detection-veri-hesaplari-cloud</loc>
    <lastmod>2026-05-13T13:04:48.024Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-veri-hesaplari-cloud"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomaly-detection-veri-hesaplari-cloud"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-veri-hesaplari-cloud"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Kursta kullanacağımız 18 veri kümesinin nereden indirileceği, Kaggle API kurulumu, HuggingFace datasets, Numenta NAB, MVTec AD, NASA Turbofan, CWRU bearing, IEEE-CIS Fraud — ve Google Colab/RunPod ile ücretsiz GPU erişimi.</image:caption>
      <image:title>Veri Hesapları &amp; Cloud: Kaggle, HuggingFace, Numenta, MVTec, NASA — Anomaly Detection Veri Cephaneliği</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/test-time-compute-scaling-snell-2024-matematik</loc>
    <lastmod>2026-05-13T13:00:30.723Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/test-time-compute-scaling-snell-2024-matematik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/test-time-compute-scaling-snell-2024-matematik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/test-time-compute-scaling-snell-2024-matematik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Yeni scaling boyutunun matematiği: Snell vd. 2024 &apos;Scaling LLM Test-Time Compute Optimally&apos; paper&apos;ı. Multi-sample (best-of-N, self-consistency) vs deep thinking (uzun reasoning chain) trade-off&apos;ları. Optimum compute allocation: aynı bütçeyi nasıl en iyi dağıtırsın? Pre-training compute ile arasındaki paradoks: %20 daha az pre-training + %50 daha çok test-time = aynı kalite. Türkçe için &apos;düşünme bütçesi&apos; planlaması.</image:caption>
      <image:title>Test-Time Compute Scaling Matematiği: Snell 2024 Paper&apos;ı — &apos;Düşünmek&apos; İçin Compute Harcamanın Yeni Bilimi</image:title>
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  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/test-time-compute-scaling-snell-2024-matematik</loc>
    <lastmod>2026-05-13T13:00:30.723Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/test-time-compute-scaling-snell-2024-matematik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/test-time-compute-scaling-snell-2024-matematik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/test-time-compute-scaling-snell-2024-matematik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Yeni scaling boyutunun matematiği: Snell vd. 2024 &apos;Scaling LLM Test-Time Compute Optimally&apos; paper&apos;ı. Multi-sample (best-of-N, self-consistency) vs deep thinking (uzun reasoning chain) trade-off&apos;ları. Optimum compute allocation: aynı bütçeyi nasıl en iyi dağıtırsın? Pre-training compute ile arasındaki paradoks: %20 daha az pre-training + %50 daha çok test-time = aynı kalite. Türkçe için &apos;düşünme bütçesi&apos; planlaması.</image:caption>
      <image:title>Test-Time Compute Scaling Matematiği: Snell 2024 Paper&apos;ı — &apos;Düşünmek&apos; İçin Compute Harcamanın Yeni Bilimi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/o1-mimari-spekulatif-analiz</loc>
    <lastmod>2026-05-13T13:00:30.810Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/o1-mimari-spekulatif-analiz"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/o1-mimari-spekulatif-analiz"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/o1-mimari-spekulatif-analiz"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>OpenAI&apos;in açıklamadığı o1 mimarisini, public observations + akademik paper&apos;lar + community reverse engineering birleştirerek tahmin ediyoruz. PRM (Process Reward Model) + MCTS (Monte Carlo Tree Search) + RL kombinasyonu mu? Pricing modelinden çıkarılan ipuçları. Reasoning tokens&apos;in görünmemesinin AI safety + ticari anlamı. R1 paper&apos;ından geri yansıma — açık alternatif ne öğretti?</image:caption>
      <image:title>o1 Mimari Spekülatif Analiz: Kapalı Kapılar Ardından — Public Observations + Reverse Engineering</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/o1-mimari-spekulatif-analiz</loc>
    <lastmod>2026-05-13T13:00:30.810Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/o1-mimari-spekulatif-analiz"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/o1-mimari-spekulatif-analiz"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/o1-mimari-spekulatif-analiz"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>OpenAI&apos;in açıklamadığı o1 mimarisini, public observations + akademik paper&apos;lar + community reverse engineering birleştirerek tahmin ediyoruz. PRM (Process Reward Model) + MCTS (Monte Carlo Tree Search) + RL kombinasyonu mu? Pricing modelinden çıkarılan ipuçları. Reasoning tokens&apos;in görünmemesinin AI safety + ticari anlamı. R1 paper&apos;ından geri yansıma — açık alternatif ne öğretti?</image:caption>
      <image:title>o1 Mimari Spekülatif Analiz: Kapalı Kapılar Ardından — Public Observations + Reverse Engineering</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/deepseek-r1-grpo-derinlemesine-matematik</loc>
    <lastmod>2026-05-13T13:00:30.896Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/deepseek-r1-grpo-derinlemesine-matematik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/deepseek-r1-grpo-derinlemesine-matematik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/deepseek-r1-grpo-derinlemesine-matematik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>DeepSeek-R1&apos;in (Ocak 2025) ana eğitim algoritması GRPO (Group Relative Policy Optimization). PPO&apos;dan farkları satır satır türev. Value function&apos;sız avantaj tahmini (grup karşılaştırması). 4 aşamalı eğitim (R1-Zero → Cold Start → Reasoning RL → Distill) detaylı walk-through. &apos;Aha moments&apos; empirik fenomeni — paper&apos;da verilen örnekler ve istatistik analiz. Türkçe için R1 fine-tune stratejileri.</image:caption>
      <image:title>DeepSeek-R1 GRPO Derinlemesine: Açık Reasoning RL&apos;in Matematiği — Group Relative Policy Optimization</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/deepseek-r1-grpo-derinlemesine-matematik</loc>
    <lastmod>2026-05-13T13:00:30.896Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/deepseek-r1-grpo-derinlemesine-matematik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/deepseek-r1-grpo-derinlemesine-matematik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/deepseek-r1-grpo-derinlemesine-matematik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>DeepSeek-R1&apos;in (Ocak 2025) ana eğitim algoritması GRPO (Group Relative Policy Optimization). PPO&apos;dan farkları satır satır türev. Value function&apos;sız avantaj tahmini (grup karşılaştırması). 4 aşamalı eğitim (R1-Zero → Cold Start → Reasoning RL → Distill) detaylı walk-through. &apos;Aha moments&apos; empirik fenomeni — paper&apos;da verilen örnekler ve istatistik analiz. Türkçe için R1 fine-tune stratejileri.</image:caption>
      <image:title>DeepSeek-R1 GRPO Derinlemesine: Açık Reasoning RL&apos;in Matematiği — Group Relative Policy Optimization</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-reasoning-model-r1-distill</loc>
    <lastmod>2026-05-13T13:00:30.984Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-reasoning-model-r1-distill"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-reasoning-model-r1-distill"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-reasoning-model-r1-distill"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 17 capstone: R1-Distill-Qwen-32B üzerine Türkçe matematik DPO fine-tune. YKS/TYT/TÜBİTAK matematik problemlerinden 5K Türkçe reasoning chain dataset oluşturma, DPO eğitim (1 H100, 1 hafta, $200-500), evaluation (AIME-TR, YKS matematik), HuggingFace Hub&apos;da yayın. Müfredatın 8. production artefaktı: sukruyusufkaya/r1-distill-tr-math-32b.</image:caption>
      <image:title>Capstone Modül 17: Türkçe Reasoning Model Üretime — R1-Distill-32B Türkçe Matematik Fine-Tune</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-reasoning-model-r1-distill</loc>
    <lastmod>2026-05-13T13:00:30.984Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-reasoning-model-r1-distill"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-reasoning-model-r1-distill"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-reasoning-model-r1-distill"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 17 capstone: R1-Distill-Qwen-32B üzerine Türkçe matematik DPO fine-tune. YKS/TYT/TÜBİTAK matematik problemlerinden 5K Türkçe reasoning chain dataset oluşturma, DPO eğitim (1 H100, 1 hafta, $200-500), evaluation (AIME-TR, YKS matematik), HuggingFace Hub&apos;da yayın. Müfredatın 8. production artefaktı: sukruyusufkaya/r1-distill-tr-math-32b.</image:caption>
      <image:title>Capstone Modül 17: Türkçe Reasoning Model Üretime — R1-Distill-32B Türkçe Matematik Fine-Tune</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/ai-veri-muhendisligi/ai-cagi-veri-muhendisi-evrimi</loc>
    <lastmod>2026-05-13T18:17:44.921Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/ai-veri-muhendisligi/ai-cagi-veri-muhendisi-evrimi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/ai-veri-muhendisligi/ai-cagi-veri-muhendisi-evrimi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/ai-veri-muhendisligi/ai-cagi-veri-muhendisi-evrimi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Veri mühendisliği 1995&apos;ten 2026&apos;ya nasıl evrimleşti? DBA, ETL Developer, Data Engineer, Analytics Engineer ve AI Data Engineer rolleri arasındaki farklar, yetkinlik matrisi, Türkiye ve global maaş aralıkları, günlük iş akışı.</image:caption>
      <image:title>AI Çağında Veri Mühendisi Kimdir? DBA&apos;dan AI Data Engineer&apos;a 30 Yıllık Evrim</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/ai-veri-muhendisligi/ai-cagi-veri-muhendisi-evrimi</loc>
    <lastmod>2026-05-13T18:17:44.921Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/ai-veri-muhendisligi/ai-cagi-veri-muhendisi-evrimi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/ai-veri-muhendisligi/ai-cagi-veri-muhendisi-evrimi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/ai-veri-muhendisligi/ai-cagi-veri-muhendisi-evrimi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Veri mühendisliği 1995&apos;ten 2026&apos;ya nasıl evrimleşti? DBA, ETL Developer, Data Engineer, Analytics Engineer ve AI Data Engineer rolleri arasındaki farklar, yetkinlik matrisi, Türkiye ve global maaş aralıkları, günlük iş akışı.</image:caption>
      <image:title>AI Çağında Veri Mühendisi Kimdir? DBA&apos;dan AI Data Engineer&apos;a 30 Yıllık Evrim</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/ai-veri-muhendisligi/kurs-yol-haritasi-11-part-34-modul</loc>
    <lastmod>2026-05-13T12:22:37.569Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/ai-veri-muhendisligi/kurs-yol-haritasi-11-part-34-modul"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/ai-veri-muhendisligi/kurs-yol-haritasi-11-part-34-modul"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/ai-veri-muhendisligi/kurs-yol-haritasi-11-part-34-modul"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>AI için Veri Mühendisliği kursunun tam yol haritası: 11 part, 34 modül, ~150 ders, 3 capstone proje. Hangi modül ne öğretiyor, hangi sırayla gitmek mantıklı, atölyeler ne içeriyor — kurs içeriğinin haritalı önizlemesi.</image:caption>
      <image:title>Bu Kursta Ne Öğreneceksin? 11 Part, 34 Modül, 3 Capstone — Tam Yol Haritası</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/ai-veri-muhendisligi/kurs-yol-haritasi-11-part-34-modul</loc>
    <lastmod>2026-05-13T12:22:37.569Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/ai-veri-muhendisligi/kurs-yol-haritasi-11-part-34-modul"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/ai-veri-muhendisligi/kurs-yol-haritasi-11-part-34-modul"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/ai-veri-muhendisligi/kurs-yol-haritasi-11-part-34-modul"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>AI için Veri Mühendisliği kursunun tam yol haritası: 11 part, 34 modül, ~150 ders, 3 capstone proje. Hangi modül ne öğretiyor, hangi sırayla gitmek mantıklı, atölyeler ne içeriyor — kurs içeriğinin haritalı önizlemesi.</image:caption>
      <image:title>Bu Kursta Ne Öğreneceksin? 11 Part, 34 Modül, 3 Capstone — Tam Yol Haritası</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/ai-veri-muhendisligi/atolye-kurulumu-docker-compose-postgres-minio-kafka-spark-jupyter</loc>
    <lastmod>2026-05-13T12:22:37.659Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/ai-veri-muhendisligi/atolye-kurulumu-docker-compose-postgres-minio-kafka-spark-jupyter"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/ai-veri-muhendisligi/atolye-kurulumu-docker-compose-postgres-minio-kafka-spark-jupyter"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/ai-veri-muhendisligi/atolye-kurulumu-docker-compose-postgres-minio-kafka-spark-jupyter"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1573164574230-db1d5e960238?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bu kursun tamamında kullanacağın profesyonel lokal veri stack&apos;ini kur: uv ile Python 3.12, Docker Compose ile Postgres 16 + pgvector + MinIO + Kafka + Spark + JupyterLab. Adım adım, hata mesajları dahil.</image:caption>
      <image:title>Atölye Kurulumu — uv + Docker Compose ile Postgres, MinIO, Kafka, Spark, Jupyter</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/ai-veri-muhendisligi/atolye-kurulumu-docker-compose-postgres-minio-kafka-spark-jupyter</loc>
    <lastmod>2026-05-13T12:22:37.659Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/ai-veri-muhendisligi/atolye-kurulumu-docker-compose-postgres-minio-kafka-spark-jupyter"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/ai-veri-muhendisligi/atolye-kurulumu-docker-compose-postgres-minio-kafka-spark-jupyter"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/ai-veri-muhendisligi/atolye-kurulumu-docker-compose-postgres-minio-kafka-spark-jupyter"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1573164574230-db1d5e960238?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bu kursun tamamında kullanacağın profesyonel lokal veri stack&apos;ini kur: uv ile Python 3.12, Docker Compose ile Postgres 16 + pgvector + MinIO + Kafka + Spark + JupyterLab. Adım adım, hata mesajları dahil.</image:caption>
      <image:title>Atölye Kurulumu — uv + Docker Compose ile Postgres, MinIO, Kafka, Spark, Jupyter</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/moe-tarihce-jacobs-1991-deepseek-v3</loc>
    <lastmod>2026-05-13T13:04:09.988Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/moe-tarihce-jacobs-1991-deepseek-v3"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/moe-tarihce-jacobs-1991-deepseek-v3"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/moe-tarihce-jacobs-1991-deepseek-v3"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Mixture of Experts&apos;in 33 yıllık entelektüel yolculuğu: Jacobs vd. 1991 orijinal paper (&apos;Adaptive Mixtures of Local Experts&apos;), Shazeer vd. 2017 &apos;Outrageously Large Neural Networks&apos; — modern MoE&apos;nin başlangıcı, GShard 2020 Google scale, Switch Transformer 2021, Mixtral 8x7B (Ocak 2024) açık kaynak devrim, DeepSeek-V3 (Aralık 2024) 671B aktif 37B. &apos;Niye 33 yıl kapı dışında kaldı, niye şimdi geri döndü?&apos;</image:caption>
      <image:title>MoE Tarihçesi: Jacobs 1991&apos;den DeepSeek-V3 2024&apos;e — 33 Yıllık Sparse Activation Devrimi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/moe-tarihce-jacobs-1991-deepseek-v3</loc>
    <lastmod>2026-05-13T13:04:09.988Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/moe-tarihce-jacobs-1991-deepseek-v3"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/moe-tarihce-jacobs-1991-deepseek-v3"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/moe-tarihce-jacobs-1991-deepseek-v3"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Mixture of Experts&apos;in 33 yıllık entelektüel yolculuğu: Jacobs vd. 1991 orijinal paper (&apos;Adaptive Mixtures of Local Experts&apos;), Shazeer vd. 2017 &apos;Outrageously Large Neural Networks&apos; — modern MoE&apos;nin başlangıcı, GShard 2020 Google scale, Switch Transformer 2021, Mixtral 8x7B (Ocak 2024) açık kaynak devrim, DeepSeek-V3 (Aralık 2024) 671B aktif 37B. &apos;Niye 33 yıl kapı dışında kaldı, niye şimdi geri döndü?&apos;</image:caption>
      <image:title>MoE Tarihçesi: Jacobs 1991&apos;den DeepSeek-V3 2024&apos;e — 33 Yıllık Sparse Activation Devrimi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/moe-matematik-anatomi-gating-routing-load-balancing</loc>
    <lastmod>2026-05-13T13:04:10.292Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/moe-matematik-anatomi-gating-routing-load-balancing"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/moe-matematik-anatomi-gating-routing-load-balancing"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/moe-matematik-anatomi-gating-routing-load-balancing"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>MoE&apos;nin iç matematiği: gating network&apos;ün türev hesabı, top-k routing&apos;in implementasyonu, expert collapse problemi ve load balancing loss (Shazeer 2017), auxiliary loss matematik, capacity factor, drop tokens, FLOP analizi. PyTorch&apos;ta sıfırdan MoE FFN layer implementation. Türkçe data&apos;da expert utilization gözlemleri.</image:caption>
      <image:title>MoE Matematik Anatomi: Gating Network, Top-k Routing, Load Balancing — Sparse Activation Sıfırdan</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/moe-matematik-anatomi-gating-routing-load-balancing</loc>
    <lastmod>2026-05-13T13:04:10.292Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/moe-matematik-anatomi-gating-routing-load-balancing"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/moe-matematik-anatomi-gating-routing-load-balancing"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/moe-matematik-anatomi-gating-routing-load-balancing"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>MoE&apos;nin iç matematiği: gating network&apos;ün türev hesabı, top-k routing&apos;in implementasyonu, expert collapse problemi ve load balancing loss (Shazeer 2017), auxiliary loss matematik, capacity factor, drop tokens, FLOP analizi. PyTorch&apos;ta sıfırdan MoE FFN layer implementation. Türkçe data&apos;da expert utilization gözlemleri.</image:caption>
      <image:title>MoE Matematik Anatomi: Gating Network, Top-k Routing, Load Balancing — Sparse Activation Sıfırdan</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/deepseek-v3-inovasyonlari-mla-multi-token</loc>
    <lastmod>2026-05-13T13:00:31.251Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/deepseek-v3-inovasyonlari-mla-multi-token"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/deepseek-v3-inovasyonlari-mla-multi-token"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/deepseek-v3-inovasyonlari-mla-multi-token"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>DeepSeek-V3&apos;ün 3 kritik yeniliği derinlemesine: (1) Multi-head Latent Attention (MLA) — KV cache&apos;i %93 azaltan attention varyantı, (2) Auxiliary-loss-free load balancing — bias trick ile temiz gating, (3) Multi-token prediction (MTP) — eğitimde 2-3 token paralel tahmin. Her birinin matematik anatomisi, niye işe yarıyor, V3&apos;ün $5.6M training cost&apos;una nasıl katkıda bulundu. Türkçe için pratik kullanım.</image:caption>
      <image:title>DeepSeek-V3 İnovasyonları: MLA, Auxiliary-Loss-Free, Multi-Token Prediction — 2024 Frontier&apos;ın 3 Anahtarı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/deepseek-v3-inovasyonlari-mla-multi-token</loc>
    <lastmod>2026-05-13T13:00:31.251Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/deepseek-v3-inovasyonlari-mla-multi-token"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/deepseek-v3-inovasyonlari-mla-multi-token"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/deepseek-v3-inovasyonlari-mla-multi-token"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>DeepSeek-V3&apos;ün 3 kritik yeniliği derinlemesine: (1) Multi-head Latent Attention (MLA) — KV cache&apos;i %93 azaltan attention varyantı, (2) Auxiliary-loss-free load balancing — bias trick ile temiz gating, (3) Multi-token prediction (MTP) — eğitimde 2-3 token paralel tahmin. Her birinin matematik anatomisi, niye işe yarıyor, V3&apos;ün $5.6M training cost&apos;una nasıl katkıda bulundu. Türkçe için pratik kullanım.</image:caption>
      <image:title>DeepSeek-V3 İnovasyonları: MLA, Auxiliary-Loss-Free, Multi-Token Prediction — 2024 Frontier&apos;ın 3 Anahtarı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-mixtral-dpo</loc>
    <lastmod>2026-05-13T13:00:31.348Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-mixtral-dpo"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-mixtral-dpo"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-mixtral-dpo"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 18 capstone: Mixtral-8x7B-Instruct üzerine Türkçe DPO fine-tune. 5K Türkçe karşılaştırma data + QLoRA-DPO + 2× H100 (FSDP) + vLLM deployment. Expert utilization Türkçe için optimize ediliyor. Maliyet $200-500. Müfredatın 9. production artefaktı: sukruyusufkaya/mixtral-8x7b-tr-dpo.</image:caption>
      <image:title>Capstone Modül 18: Türkçe Mixtral DPO — Açık MoE&apos;yi Türkçeye Bük</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-mixtral-dpo</loc>
    <lastmod>2026-05-13T13:00:31.348Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-mixtral-dpo"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-mixtral-dpo"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-mixtral-dpo"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 18 capstone: Mixtral-8x7B-Instruct üzerine Türkçe DPO fine-tune. 5K Türkçe karşılaştırma data + QLoRA-DPO + 2× H100 (FSDP) + vLLM deployment. Expert utilization Türkçe için optimize ediliyor. Maliyet $200-500. Müfredatın 9. production artefaktı: sukruyusufkaya/mixtral-8x7b-tr-dpo.</image:caption>
      <image:title>Capstone Modül 18: Türkçe Mixtral DPO — Açık MoE&apos;yi Türkçeye Bük</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/anomali-outlier-novelty-noise-farklari</loc>
    <lastmod>2026-05-13T13:04:48.112Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomali-outlier-novelty-noise-farklari"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomali-outlier-novelty-noise-farklari"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomali-outlier-novelty-noise-farklari"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Akademik literatürde ve sektörde sık sık birbirinin yerine kullanılan anomali, outlier, novelty ve noise kavramları arasındaki kesin farklar; Hawkins tanımı; bu farklar neden production&apos;da kritik?</image:caption>
      <image:title>Anomali, Outlier, Novelty, Noise: Birbirine Karıştırılan Dört Kavramın Hassas Farkları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomali-outlier-novelty-noise-farklari</loc>
    <lastmod>2026-05-13T13:04:48.112Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomali-outlier-novelty-noise-farklari"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomali-outlier-novelty-noise-farklari"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomali-outlier-novelty-noise-farklari"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Akademik literatürde ve sektörde sık sık birbirinin yerine kullanılan anomali, outlier, novelty ve noise kavramları arasındaki kesin farklar; Hawkins tanımı; bu farklar neden production&apos;da kritik?</image:caption>
      <image:title>Anomali, Outlier, Novelty, Noise: Birbirine Karıştırılan Dört Kavramın Hassas Farkları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/uc-anomali-tipi-point-contextual-collective</loc>
    <lastmod>2026-05-13T13:04:48.199Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/uc-anomali-tipi-point-contextual-collective"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/uc-anomali-tipi-point-contextual-collective"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/uc-anomali-tipi-point-contextual-collective"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anomalilerin üç temel tipi: nokta anomalileri (point), bağlamsal anomaliler (contextual) ve toplu anomaliler (collective). Her tip için 6 sektörel örnek, görsel sezgi ve uygun yöntem haritası.</image:caption>
      <image:title>Üç Anomali Tipi: Point, Contextual ve Collective — Hangi Yöntem Hangisi İçin?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/uc-anomali-tipi-point-contextual-collective</loc>
    <lastmod>2026-05-13T13:04:48.199Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/uc-anomali-tipi-point-contextual-collective"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/uc-anomali-tipi-point-contextual-collective"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/uc-anomali-tipi-point-contextual-collective"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anomalilerin üç temel tipi: nokta anomalileri (point), bağlamsal anomaliler (contextual) ve toplu anomaliler (collective). Her tip için 6 sektörel örnek, görsel sezgi ve uygun yöntem haritası.</image:caption>
      <image:title>Üç Anomali Tipi: Point, Contextual ve Collective — Hangi Yöntem Hangisi İçin?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/anomali-tespiti-ogrenme-rejimleri</loc>
    <lastmod>2026-05-13T13:04:48.283Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomali-tespiti-ogrenme-rejimleri"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomali-tespiti-ogrenme-rejimleri"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomali-tespiti-ogrenme-rejimleri"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anomaly detection için dört öğrenme rejimi: supervised, semi-supervised, unsupervised, weakly-supervised. Etiket pahalılığı tablosu, hangi sektörde hangi rejim, ve hibrit yaklaşımlar.</image:caption>
      <image:title>Öğrenme Rejimleri: Supervised, Semi-Supervised, Unsupervised, Weakly-Supervised — Etiket Kıtlığı Altında Karar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomali-tespiti-ogrenme-rejimleri</loc>
    <lastmod>2026-05-13T13:04:48.283Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomali-tespiti-ogrenme-rejimleri"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomali-tespiti-ogrenme-rejimleri"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomali-tespiti-ogrenme-rejimleri"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anomaly detection için dört öğrenme rejimi: supervised, semi-supervised, unsupervised, weakly-supervised. Etiket pahalılığı tablosu, hangi sektörde hangi rejim, ve hibrit yaklaşımlar.</image:caption>
      <image:title>Öğrenme Rejimleri: Supervised, Semi-Supervised, Unsupervised, Weakly-Supervised — Etiket Kıtlığı Altında Karar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-pipeline-anatomisi</loc>
    <lastmod>2026-05-13T13:04:48.373Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-pipeline-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomaly-detection-pipeline-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-pipeline-anatomisi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1542903660-eedba2cda473?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Production-grade anomaly detection pipeline&apos;ının 7 katmanı: ingestion, feature engineering, scoring, thresholding, alerting, feedback loop, monitoring. Her katmanda kritik kararlar ve ölçüm noktaları.</image:caption>
      <image:title>Anomaly Detection Pipeline Anatomisi: Ingestion&apos;dan Alarm&apos;a Uçtan Uca 7 Katman</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomaly-detection-pipeline-anatomisi</loc>
    <lastmod>2026-05-13T13:04:48.373Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-pipeline-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/anomaly-detection-pipeline-anatomisi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/anomaly-detection-pipeline-anatomisi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1542903660-eedba2cda473?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Production-grade anomaly detection pipeline&apos;ının 7 katmanı: ingestion, feature engineering, scoring, thresholding, alerting, feedback loop, monitoring. Her katmanda kritik kararlar ve ölçüm noktaları.</image:caption>
      <image:title>Anomaly Detection Pipeline Anatomisi: Ingestion&apos;dan Alarm&apos;a Uçtan Uca 7 Katman</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/hands-on-uc-anomali-tipi-gorsellestirme</loc>
    <lastmod>2026-05-13T13:04:48.478Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/hands-on-uc-anomali-tipi-gorsellestirme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/hands-on-uc-anomali-tipi-gorsellestirme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/hands-on-uc-anomali-tipi-gorsellestirme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Pratik laboratuvar: Python ile sentetik veri üreterek üç anomali tipini (point, contextual, collective) görselleştir; iForest, Prophet residual ve LSTM-AE ile her tipi tespit et; interaktif Plotly dashboard&apos;u kur.</image:caption>
      <image:title>Hands-on Lab: Üç Anomali Tipini Sentetik Veriyle Görselleştirme — Python + Matplotlib + Plotly</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/hands-on-uc-anomali-tipi-gorsellestirme</loc>
    <lastmod>2026-05-13T13:04:48.478Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/hands-on-uc-anomali-tipi-gorsellestirme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/hands-on-uc-anomali-tipi-gorsellestirme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1526379095098-d400fd0bf935?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Pratik laboratuvar: Python ile sentetik veri üreterek üç anomali tipini (point, contextual, collective) görselleştir; iForest, Prophet residual ve LSTM-AE ile her tipi tespit et; interaktif Plotly dashboard&apos;u kur.</image:caption>
      <image:title>Hands-on Lab: Üç Anomali Tipini Sentetik Veriyle Görselleştirme — Python + Matplotlib + Plotly</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/multimodal-tarihce-clip-gpt-4o</loc>
    <lastmod>2026-05-13T13:00:31.432Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
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    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/multimodal-tarihce-clip-gpt-4o"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Multimodal LLM&apos;lerin tarihsel ve kavramsal anatomisi: Radford vd. 2021 CLIP paper&apos;ı — contrastive learning ile resim-metin alignment&apos;ın doğuşu, ViT (Dosovitskiy 2020) image transformer, BLIP (Li 2022), Flamingo (Alayrac 2022), LLaVA (Liu 2023) open-source çığır, GPT-4V (Eylül 2023), GPT-4o (Mayıs 2024) unified omni-modal, Llama-3.2 Vision (Eylül 2024) açık-kaynak. 5 yıllık &apos;dil + görüntü&apos; birleşme yolculuğu ve Türkçe için multimodal ne ifade ediyor (Türkçe doküman OCR, kültürel görsel anlama).</image:caption>
      <image:title>Multimodal LLM Tarihçesi: Radford 2021 CLIP&apos;ten GPT-4o&apos;ya — &apos;Görmeyi Öğrenen&apos; Dil Modellerinin Doğuşu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/multimodal-tarihce-clip-gpt-4o</loc>
    <lastmod>2026-05-13T13:00:31.432Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/multimodal-tarihce-clip-gpt-4o"/>
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    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/multimodal-tarihce-clip-gpt-4o"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Multimodal LLM&apos;lerin tarihsel ve kavramsal anatomisi: Radford vd. 2021 CLIP paper&apos;ı — contrastive learning ile resim-metin alignment&apos;ın doğuşu, ViT (Dosovitskiy 2020) image transformer, BLIP (Li 2022), Flamingo (Alayrac 2022), LLaVA (Liu 2023) open-source çığır, GPT-4V (Eylül 2023), GPT-4o (Mayıs 2024) unified omni-modal, Llama-3.2 Vision (Eylül 2024) açık-kaynak. 5 yıllık &apos;dil + görüntü&apos; birleşme yolculuğu ve Türkçe için multimodal ne ifade ediyor (Türkçe doküman OCR, kültürel görsel anlama).</image:caption>
      <image:title>Multimodal LLM Tarihçesi: Radford 2021 CLIP&apos;ten GPT-4o&apos;ya — &apos;Görmeyi Öğrenen&apos; Dil Modellerinin Doğuşu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/multimodal-mimari-matematik-vision-llm-baglama</loc>
    <lastmod>2026-05-13T13:00:31.526Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/multimodal-mimari-matematik-vision-llm-baglama"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/multimodal-mimari-matematik-vision-llm-baglama"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/multimodal-mimari-matematik-vision-llm-baglama"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Multimodal LLM&apos;lerin iç mimari matematiği: Vision encoder (ViT/CLIP/SigLIP) → projection → LLM bağlama 3 stratejisi. (1) Linear projection (LLaVA tarzı, basit), (2) Q-Former (BLIP-2 tarzı, learnable queries), (3) Cross-attention (Flamingo/Llama-3.2 tarzı, derin entegrasyon). Image token budget management, resolution sorunu, vision-text alignment. PyTorch&apos;ta sıfırdan LLaVA-style multimodal mimari. Türkçe için image-text alignment.</image:caption>
      <image:title>Multimodal Mimari Matematiği: Vision Encoder → Projection → LLM — 3 Bağlama Stratejisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/multimodal-mimari-matematik-vision-llm-baglama</loc>
    <lastmod>2026-05-13T13:00:31.526Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/multimodal-mimari-matematik-vision-llm-baglama"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/multimodal-mimari-matematik-vision-llm-baglama"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/multimodal-mimari-matematik-vision-llm-baglama"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Multimodal LLM&apos;lerin iç mimari matematiği: Vision encoder (ViT/CLIP/SigLIP) → projection → LLM bağlama 3 stratejisi. (1) Linear projection (LLaVA tarzı, basit), (2) Q-Former (BLIP-2 tarzı, learnable queries), (3) Cross-attention (Flamingo/Llama-3.2 tarzı, derin entegrasyon). Image token budget management, resolution sorunu, vision-text alignment. PyTorch&apos;ta sıfırdan LLaVA-style multimodal mimari. Türkçe için image-text alignment.</image:caption>
      <image:title>Multimodal Mimari Matematiği: Vision Encoder → Projection → LLM — 3 Bağlama Stratejisi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/turkce-multimodal-pratik-kimlik-fatura-trafik</loc>
    <lastmod>2026-05-13T13:00:31.610Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/turkce-multimodal-pratik-kimlik-fatura-trafik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/turkce-multimodal-pratik-kimlik-fatura-trafik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/turkce-multimodal-pratik-kimlik-fatura-trafik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Türkçe multimodal LLM&apos;lerin production kullanım alanları: (1) Kimlik kartı + ehliyet OCR + alan çıkarma (bankacılık, telco), (2) E-fatura + makbuz processing (muhasebe), (3) Türkçe trafik işaretleri tanıma (otomotiv), (4) Türkçe sınav kağıdı dijitalleştirme (eğitim), (5) Osmanlıca belge analizi (akademik). Her use case için GPT-4o vs Llama-3.2-Vision karşılaştırma, KVKK uyumlu pipeline, Python production code. Türkçe için multimodal prompting best practices.</image:caption>
      <image:title>Türkçe Multimodal Pratiği: Kimlik OCR&apos;dan Trafik İşaretine — 5 Production Use Case</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/turkce-multimodal-pratik-kimlik-fatura-trafik</loc>
    <lastmod>2026-05-13T13:00:31.610Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/turkce-multimodal-pratik-kimlik-fatura-trafik"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/turkce-multimodal-pratik-kimlik-fatura-trafik"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/turkce-multimodal-pratik-kimlik-fatura-trafik"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Türkçe multimodal LLM&apos;lerin production kullanım alanları: (1) Kimlik kartı + ehliyet OCR + alan çıkarma (bankacılık, telco), (2) E-fatura + makbuz processing (muhasebe), (3) Türkçe trafik işaretleri tanıma (otomotiv), (4) Türkçe sınav kağıdı dijitalleştirme (eğitim), (5) Osmanlıca belge analizi (akademik). Her use case için GPT-4o vs Llama-3.2-Vision karşılaştırma, KVKK uyumlu pipeline, Python production code. Türkçe için multimodal prompting best practices.</image:caption>
      <image:title>Türkçe Multimodal Pratiği: Kimlik OCR&apos;dan Trafik İşaretine — 5 Production Use Case</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-multimodal-dokuman-isleme</loc>
    <lastmod>2026-05-13T13:00:31.709Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-multimodal-dokuman-isleme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-multimodal-dokuman-isleme"/>
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      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 19 capstone: Türkçe multimodal doküman işleme production SaaS. Next.js drag-drop frontend + FastAPI backend + Llama-3.2-Vision veya GPT-4o seçilebilir model + KVKK uyumlu encrypted storage + Stripe payment. Kimlik OCR, e-fatura, sınav kağıdı, ücretsiz tier + premium. Müfredatın 10. production artefaktı: docproc.sukruyusufkaya.com.</image:caption>
      <image:title>Capstone Modül 19: Türkçe Multimodal Doküman İşleme Sistemi — Production SaaS</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-multimodal-dokuman-isleme</loc>
    <lastmod>2026-05-13T13:00:31.709Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-multimodal-dokuman-isleme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-multimodal-dokuman-isleme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-multimodal-dokuman-isleme"/>
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      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 19 capstone: Türkçe multimodal doküman işleme production SaaS. Next.js drag-drop frontend + FastAPI backend + Llama-3.2-Vision veya GPT-4o seçilebilir model + KVKK uyumlu encrypted storage + Stripe payment. Kimlik OCR, e-fatura, sınav kağıdı, ücretsiz tier + premium. Müfredatın 10. production artefaktı: docproc.sukruyusufkaya.com.</image:caption>
      <image:title>Capstone Modül 19: Türkçe Multimodal Doküman İşleme Sistemi — Production SaaS</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/tool-use-tarihce-react-mcp</loc>
    <lastmod>2026-05-13T13:00:31.795Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tool-use-tarihce-react-mcp"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tool-use-tarihce-react-mcp"/>
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      <image:loc>https://images.unsplash.com/photo-1542903660-eedba2cda473?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM ajanlarının tarihsel ve kavramsal anatomisi: Yao vd. 2022 ReAct paper&apos;ı (&apos;Reasoning + Action&apos; birleşmesi), OpenAI function calling (Haziran 2023, ilk standartlaşma), Anthropic MCP (Kasım 2024, açık standart). LangChain, AutoGen, CrewAI gibi framework&apos;lerin yükselişi. &apos;Niye LLM&apos;ler kendi başına yeterli değil, niye tool kullanmaları gerekiyor?&apos; AGI tartışmasının pratik yüzü. Türkçe ajan use case&apos;leri.</image:caption>
      <image:title>Tool Use Tarihçesi: Yao 2022 ReAct&apos;tan Anthropic MCP&apos;ye — LLM Ajanlarının 3 Yıllık Doğuşu</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tool-use-tarihce-react-mcp</loc>
    <lastmod>2026-05-13T13:00:31.795Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tool-use-tarihce-react-mcp"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tool-use-tarihce-react-mcp"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tool-use-tarihce-react-mcp"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1542903660-eedba2cda473?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM ajanlarının tarihsel ve kavramsal anatomisi: Yao vd. 2022 ReAct paper&apos;ı (&apos;Reasoning + Action&apos; birleşmesi), OpenAI function calling (Haziran 2023, ilk standartlaşma), Anthropic MCP (Kasım 2024, açık standart). LangChain, AutoGen, CrewAI gibi framework&apos;lerin yükselişi. &apos;Niye LLM&apos;ler kendi başına yeterli değil, niye tool kullanmaları gerekiyor?&apos; AGI tartışmasının pratik yüzü. Türkçe ajan use case&apos;leri.</image:caption>
      <image:title>Tool Use Tarihçesi: Yao 2022 ReAct&apos;tan Anthropic MCP&apos;ye — LLM Ajanlarının 3 Yıllık Doğuşu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/tool-use-matematik-implementation-pydantic</loc>
    <lastmod>2026-05-13T13:00:31.880Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tool-use-matematik-implementation-pydantic"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tool-use-matematik-implementation-pydantic"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tool-use-matematik-implementation-pydantic"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1623282033815-40b05d96c903?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tool use&apos;un iç matematiği ve production implementation: JSON schema standardı detayı, OpenAI function calling tam anatomisi, ReAct prompt mühendisliği teknikleri, MCP protokol implementation (Python stdio + SSE). Türkçe tool calling örnekleri (TC kimlik validasyonu, e-fatura sorgulama). Pydantic AI ile temiz, type-safe ajan. LangChain alternatifi olarak modern yaklaşım. Error handling, retry logic, tool timeout management.</image:caption>
      <image:title>Tool Use Matematik ve Implementation: JSON Schema&apos;dan Pydantic AI&apos;a — Production Ajan Mühendisliği</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tool-use-matematik-implementation-pydantic</loc>
    <lastmod>2026-05-13T13:00:31.880Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tool-use-matematik-implementation-pydantic"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/tool-use-matematik-implementation-pydantic"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/tool-use-matematik-implementation-pydantic"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1623282033815-40b05d96c903?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tool use&apos;un iç matematiği ve production implementation: JSON schema standardı detayı, OpenAI function calling tam anatomisi, ReAct prompt mühendisliği teknikleri, MCP protokol implementation (Python stdio + SSE). Türkçe tool calling örnekleri (TC kimlik validasyonu, e-fatura sorgulama). Pydantic AI ile temiz, type-safe ajan. LangChain alternatifi olarak modern yaklaşım. Error handling, retry logic, tool timeout management.</image:caption>
      <image:title>Tool Use Matematik ve Implementation: JSON Schema&apos;dan Pydantic AI&apos;a — Production Ajan Mühendisliği</image:title>
    </image:image>
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    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-e-ticaret-multi-agent-crewai</loc>
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    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-e-ticaret-multi-agent-crewai"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-e-ticaret-multi-agent-crewai"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-e-ticaret-multi-agent-crewai"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1623282033815-40b05d96c903?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 20 capstone: Türkçe e-ticaret multi-agent sistemi. 3 ajan: (1) Research Agent — Trendyol/Hepsiburada&apos;da ürün arama, (2) Price Compare Agent — fiyat ve kargo karşılaştırma, (3) Recommendation Agent — kullanıcıya öneri. CrewAI framework, Pydantic AI tools, FastAPI backend, Next.js frontend, Stripe API. Türkçe doğal sohbet → otomatik alışveriş araştırması. KVKK uyumlu. Müfredatın 11. production artefaktı.</image:caption>
      <image:title>Capstone Modül 20: Türkçe E-Ticaret Multi-Agent Sistemi — CrewAI ile Production Ajan</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-e-ticaret-multi-agent-crewai</loc>
    <lastmod>2026-05-13T13:00:31.966Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-e-ticaret-multi-agent-crewai"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-e-ticaret-multi-agent-crewai"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-e-ticaret-multi-agent-crewai"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1623282033815-40b05d96c903?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 20 capstone: Türkçe e-ticaret multi-agent sistemi. 3 ajan: (1) Research Agent — Trendyol/Hepsiburada&apos;da ürün arama, (2) Price Compare Agent — fiyat ve kargo karşılaştırma, (3) Recommendation Agent — kullanıcıya öneri. CrewAI framework, Pydantic AI tools, FastAPI backend, Next.js frontend, Stripe API. Türkçe doğal sohbet → otomatik alışveriş araştırması. KVKK uyumlu. Müfredatın 11. production artefaktı.</image:caption>
      <image:title>Capstone Modül 20: Türkçe E-Ticaret Multi-Agent Sistemi — CrewAI ile Production Ajan</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/ml-pipeline-veri-yeri-dongusu</loc>
    <lastmod>2026-05-13T12:53:32.222Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/ml-pipeline-veri-yeri-dongusu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/ml-pipeline-veri-yeri-dongusu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/ml-pipeline-veri-yeri-dongusu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1460925895917-afdab827c52f?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir makine öğrenmesi sisteminin tam yaşam döngüsü: veri toplama → etiketleme → eğitim → değerlendirme → üretim → izleme → geri toplama. Her aşamanın veri etiketlemeyle ilişkisi, geri bildirim döngüsü, sürekli iyileştirme ve neden &quot;data flywheel&quot; modern AI&apos;ın ana rekabet avantajıdır.</image:caption>
      <image:title>ML Pipeline&apos;da Verinin Yeri: Toplama, Etiketleme, Eğitim, Değerlendirme, Üretim Döngüsü</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/ml-pipeline-veri-yeri-dongusu</loc>
    <lastmod>2026-05-13T12:53:32.222Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/ml-pipeline-veri-yeri-dongusu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/ml-pipeline-veri-yeri-dongusu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/ml-pipeline-veri-yeri-dongusu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1460925895917-afdab827c52f?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir makine öğrenmesi sisteminin tam yaşam döngüsü: veri toplama → etiketleme → eğitim → değerlendirme → üretim → izleme → geri toplama. Her aşamanın veri etiketlemeyle ilişkisi, geri bildirim döngüsü, sürekli iyileştirme ve neden &quot;data flywheel&quot; modern AI&apos;ın ana rekabet avantajıdır.</image:caption>
      <image:title>ML Pipeline&apos;da Verinin Yeri: Toplama, Etiketleme, Eğitim, Değerlendirme, Üretim Döngüsü</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/etiketleme-turleri-tam-taksonomi</loc>
    <lastmod>2026-05-13T12:53:32.313Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/etiketleme-turleri-tam-taksonomi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/etiketleme-turleri-tam-taksonomi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/etiketleme-turleri-tam-taksonomi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Veri etiketlemenin 14 ana format çeşidi: tekli sınıflandırma, çoklu etiket, sıralı (ordinal), NER, span, BBox, polygon, segmentation, keypoint, ranking, preference, free-form, structured ve hibrit. Her format için kullanım alanları, tooling, tipik metrikler ve hatalar.</image:caption>
      <image:title>Etiketleme Türlerinin Tam Taksonomisi: Classification&apos;dan Preference&apos;a 14 Format</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/etiketleme-turleri-tam-taksonomi</loc>
    <lastmod>2026-05-13T12:53:32.313Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/etiketleme-turleri-tam-taksonomi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/etiketleme-turleri-tam-taksonomi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/etiketleme-turleri-tam-taksonomi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Veri etiketlemenin 14 ana format çeşidi: tekli sınıflandırma, çoklu etiket, sıralı (ordinal), NER, span, BBox, polygon, segmentation, keypoint, ranking, preference, free-form, structured ve hibrit. Her format için kullanım alanları, tooling, tipik metrikler ve hatalar.</image:caption>
      <image:title>Etiketleme Türlerinin Tam Taksonomisi: Classification&apos;dan Preference&apos;a 14 Format</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/supervised-semi-self-supervised-veri-etiketleme</loc>
    <lastmod>2026-05-13T13:03:22.514Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/supervised-semi-self-supervised-veri-etiketleme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/supervised-semi-self-supervised-veri-etiketleme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/supervised-semi-self-supervised-veri-etiketleme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1505373877841-8d25f7d46678?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modern AI&apos;ın beş büyük öğrenme paradigması — supervised, semi-supervised, self-supervised, weakly supervised, ve few-shot/in-context — her birinin veri etiketleme ihtiyacı, maliyet profili ve nerede kullanılması gerektiği.</image:caption>
      <image:title>Supervised, Semi-supervised, Self-supervised: Etiketleme İhtiyacı Paradigmalara Göre Nasıl Değişir?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/supervised-semi-self-supervised-veri-etiketleme</loc>
    <lastmod>2026-05-13T13:03:22.514Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/supervised-semi-self-supervised-veri-etiketleme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/supervised-semi-self-supervised-veri-etiketleme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/supervised-semi-self-supervised-veri-etiketleme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1505373877841-8d25f7d46678?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modern AI&apos;ın beş büyük öğrenme paradigması — supervised, semi-supervised, self-supervised, weakly supervised, ve few-shot/in-context — her birinin veri etiketleme ihtiyacı, maliyet profili ve nerede kullanılması gerektiği.</image:caption>
      <image:title>Supervised, Semi-supervised, Self-supervised: Etiketleme İhtiyacı Paradigmalara Göre Nasıl Değişir?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/schema-karsilastirma-binary-multiclass-hierarchical</loc>
    <lastmod>2026-05-13T13:03:21.941Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/schema-karsilastirma-binary-multiclass-hierarchical"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/schema-karsilastirma-binary-multiclass-hierarchical"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/schema-karsilastirma-binary-multiclass-hierarchical"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Aynı 1.000 Türkçe yorum dataseti üzerinde üç farklı schema (binary positive/negative, 5-class fine-grained, hierarchical) ile etiketleme yap, model eğit ve performans+maliyet+kullanışlılık karşılaştırması yap. Bu, schema kararının pratik etkisini gösteren tam bir vaka çalışmasıdır.</image:caption>
      <image:title>[VAKA] Aynı Veriyi 3 Farklı Schema ile Etiketle: Binary, Multi-class, Hierarchical Karşılaştırma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/schema-karsilastirma-binary-multiclass-hierarchical</loc>
    <lastmod>2026-05-13T13:03:21.941Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/schema-karsilastirma-binary-multiclass-hierarchical"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/schema-karsilastirma-binary-multiclass-hierarchical"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/schema-karsilastirma-binary-multiclass-hierarchical"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Aynı 1.000 Türkçe yorum dataseti üzerinde üç farklı schema (binary positive/negative, 5-class fine-grained, hierarchical) ile etiketleme yap, model eğit ve performans+maliyet+kullanışlılık karşılaştırması yap. Bu, schema kararının pratik etkisini gösteren tam bir vaka çalışmasıdır.</image:caption>
      <image:title>[VAKA] Aynı Veriyi 3 Farklı Schema ile Etiketle: Binary, Multi-class, Hierarchical Karşılaştırma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/ground-truth-illuzyonu-annotator-subjectivity</loc>
    <lastmod>2026-05-13T13:02:10.428Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/ground-truth-illuzyonu-annotator-subjectivity"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/ground-truth-illuzyonu-annotator-subjectivity"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/ground-truth-illuzyonu-annotator-subjectivity"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1522071820081-009f0129c71c?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Veri etiketlemenin felsefi temeli: ground truth gerçekte var mı, annotator subjectivity neden kaçınılmaz, &quot;doğru cevap&quot; varsayımının modern AI&apos;da yarattığı sorunlar ve disagreement&apos;i sinyal olarak görmenin yeni paradigması.</image:caption>
      <image:title>Ground Truth İllüzyonu: &quot;Doğru Etiket&quot; Diye Bir Şey Gerçekten Var mı?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/ground-truth-illuzyonu-annotator-subjectivity</loc>
    <lastmod>2026-05-13T13:02:10.428Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/ground-truth-illuzyonu-annotator-subjectivity"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/veri-etiketleme-kalite/ground-truth-illuzyonu-annotator-subjectivity"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/veri-etiketleme-kalite/ground-truth-illuzyonu-annotator-subjectivity"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1522071820081-009f0129c71c?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Veri etiketlemenin felsefi temeli: ground truth gerçekte var mı, annotator subjectivity neden kaçınılmaz, &quot;doğru cevap&quot; varsayımının modern AI&apos;da yarattığı sorunlar ve disagreement&apos;i sinyal olarak görmenin yeni paradigması.</image:caption>
      <image:title>Ground Truth İllüzyonu: &quot;Doğru Etiket&quot; Diye Bir Şey Gerçekten Var mı?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/normal-dagilim-zscore-mad</loc>
    <lastmod>2026-05-13T13:08:08.402Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/normal-dagilim-zscore-mad"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/normal-dagilim-zscore-mad"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/normal-dagilim-zscore-mad"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Normal dağılımın anomaly detection için anlamı; z-score formülü, sezgisi ve sınırları; modified z-score ve MAD (Median Absolute Deviation) — outlier&apos;a dirençli alternatifler; from-scratch Python implementasyon.</image:caption>
      <image:title>Normal Dağılım, Z-Score, Modified Z-Score ve MAD: Anomaly Detection&apos;ın İstatistiksel Aleti</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/normal-dagilim-zscore-mad</loc>
    <lastmod>2026-05-13T13:08:08.402Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/normal-dagilim-zscore-mad"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/normal-dagilim-zscore-mad"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/normal-dagilim-zscore-mad"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Normal dağılımın anomaly detection için anlamı; z-score formülü, sezgisi ve sınırları; modified z-score ve MAD (Median Absolute Deviation) — outlier&apos;a dirençli alternatifler; from-scratch Python implementasyon.</image:caption>
      <image:title>Normal Dağılım, Z-Score, Modified Z-Score ve MAD: Anomaly Detection&apos;ın İstatistiksel Aleti</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/iqr-tukey-adjusted-boxplot</loc>
    <lastmod>2026-05-13T13:04:48.656Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/iqr-tukey-adjusted-boxplot"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/iqr-tukey-adjusted-boxplot"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/iqr-tukey-adjusted-boxplot"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1574169208507-84376144848b?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Interquartile Range (IQR), Tukey&apos;s fences (k=1.5 / k=3), boxplot anatomi, ve skewed (asimetrik) veride medcouple ile adjusted boxplot — z-score&apos;un işe yaramadığı yerlerde robust alternatifler.</image:caption>
      <image:title>IQR, Tukey&apos;s Fences ve Adjusted Boxplot: Skewed Veride Outlier Tespiti</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/iqr-tukey-adjusted-boxplot</loc>
    <lastmod>2026-05-13T13:04:48.656Z</lastmod>
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    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/iqr-tukey-adjusted-boxplot"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/iqr-tukey-adjusted-boxplot"/>
    <image:image>
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      <image:caption>Interquartile Range (IQR), Tukey&apos;s fences (k=1.5 / k=3), boxplot anatomi, ve skewed (asimetrik) veride medcouple ile adjusted boxplot — z-score&apos;un işe yaramadığı yerlerde robust alternatifler.</image:caption>
      <image:title>IQR, Tukey&apos;s Fences ve Adjusted Boxplot: Skewed Veride Outlier Tespiti</image:title>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/grubbs-dixon-esd-testleri</loc>
    <lastmod>2026-05-13T13:04:48.746Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/grubbs-dixon-esd-testleri"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/grubbs-dixon-esd-testleri"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/grubbs-dixon-esd-testleri"/>
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      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Klasik istatistiksel hipotez testleri ile outlier tespiti: Grubbs test (tek outlier), Dixon Q-test (küçük örneklem), Generalized ESD (çoklu outlier) — p-değer, formüller, scipy implementasyonu, ve hangi test ne zaman.</image:caption>
      <image:title>Grubbs, Dixon ve Generalized ESD: Outlier Tespitini Hipotez Testine Çevirmek</image:title>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/grubbs-dixon-esd-testleri</loc>
    <lastmod>2026-05-13T13:04:48.746Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/grubbs-dixon-esd-testleri"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/grubbs-dixon-esd-testleri"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/grubbs-dixon-esd-testleri"/>
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      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Klasik istatistiksel hipotez testleri ile outlier tespiti: Grubbs test (tek outlier), Dixon Q-test (küçük örneklem), Generalized ESD (çoklu outlier) — p-değer, formüller, scipy implementasyonu, ve hangi test ne zaman.</image:caption>
      <image:title>Grubbs, Dixon ve Generalized ESD: Outlier Tespitini Hipotez Testine Çevirmek</image:title>
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  <url>
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    <lastmod>2026-05-13T13:04:48.832Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/chebyshev-evt-pot-uc-olaylar"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/chebyshev-evt-pot-uc-olaylar"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/chebyshev-evt-pot-uc-olaylar"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Normal varsayımı geçmediğinde: Chebyshev eşitsizliği ile dağılım-agnostik sınır; Extreme Value Theory (block maxima, GEV); Peak Over Threshold (POT) ile Generalized Pareto Distribution — banking ve telekomda baş aktör.</image:caption>
      <image:title>Chebyshev, Extreme Value Theory ve Peak Over Threshold: Uç Olayların İstatistiği</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/chebyshev-evt-pot-uc-olaylar</loc>
    <lastmod>2026-05-13T13:04:48.832Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/chebyshev-evt-pot-uc-olaylar"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/chebyshev-evt-pot-uc-olaylar"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/chebyshev-evt-pot-uc-olaylar"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Normal varsayımı geçmediğinde: Chebyshev eşitsizliği ile dağılım-agnostik sınır; Extreme Value Theory (block maxima, GEV); Peak Over Threshold (POT) ile Generalized Pareto Distribution — banking ve telekomda baş aktör.</image:caption>
      <image:title>Chebyshev, Extreme Value Theory ve Peak Over Threshold: Uç Olayların İstatistiği</image:title>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/robust-istatistik-huber-m-estimator-mcd</loc>
    <lastmod>2026-05-13T13:04:48.919Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/robust-istatistik-huber-m-estimator-mcd"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/robust-istatistik-huber-m-estimator-mcd"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/robust-istatistik-huber-m-estimator-mcd"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Klasik istatistiğin outlier&apos;a karşı kırılganlığı; robust istatistik felsefesi; M-estimator çatısı; Huber ve Tukey biweight loss; Minimum Covariance Determinant (MCD) ile robust çok-değişkenli tahmin — modern AD&apos;nin gizli temeli.</image:caption>
      <image:title>Robust İstatistikler: Huber, M-Estimator, Tukey Biweight ve MCD — Outlier&apos;a Dirençli Tahmin</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/robust-istatistik-huber-m-estimator-mcd</loc>
    <lastmod>2026-05-13T13:04:48.919Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/robust-istatistik-huber-m-estimator-mcd"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/robust-istatistik-huber-m-estimator-mcd"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/robust-istatistik-huber-m-estimator-mcd"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Klasik istatistiğin outlier&apos;a karşı kırılganlığı; robust istatistik felsefesi; M-estimator çatısı; Huber ve Tukey biweight loss; Minimum Covariance Determinant (MCD) ile robust çok-değişkenli tahmin — modern AD&apos;nin gizli temeli.</image:caption>
      <image:title>Robust İstatistikler: Huber, M-Estimator, Tukey Biweight ve MCD — Outlier&apos;a Dirençli Tahmin</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/hands-on-nyc-taxi-5-detektor-benchmark</loc>
    <lastmod>2026-05-13T13:04:49.004Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/hands-on-nyc-taxi-5-detektor-benchmark"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/hands-on-nyc-taxi-5-detektor-benchmark"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/hands-on-nyc-taxi-5-detektor-benchmark"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1460925895917-afdab827c52f?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Numenta NAB benchmark&apos;ından NYC Taxi saatlik talep verisi: z-score, modified z, IQR, adjusted boxplot ve POT detektörlerini yan yana koşturup PR-AUC karşılaştırması — kursun ilk gerçek dataset hands-on lab&apos;ı.</image:caption>
      <image:title>Hands-on Lab: NYC Taxi Talep Anomalisinde 5 İstatistiksel Detektör Karşılaştırma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/hands-on-nyc-taxi-5-detektor-benchmark</loc>
    <lastmod>2026-05-13T13:04:49.004Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/hands-on-nyc-taxi-5-detektor-benchmark"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/hands-on-nyc-taxi-5-detektor-benchmark"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/hands-on-nyc-taxi-5-detektor-benchmark"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1460925895917-afdab827c52f?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Numenta NAB benchmark&apos;ından NYC Taxi saatlik talep verisi: z-score, modified z, IQR, adjusted boxplot ve POT detektörlerini yan yana koşturup PR-AUC karşılaştırması — kursun ilk gerçek dataset hands-on lab&apos;ı.</image:caption>
      <image:title>Hands-on Lab: NYC Taxi Talep Anomalisinde 5 İstatistiksel Detektör Karşılaştırma</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/benchmark-anatomi-mmlu-arena</loc>
    <lastmod>2026-05-13T13:00:32.050Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/benchmark-anatomi-mmlu-arena"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/benchmark-anatomi-mmlu-arena"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/benchmark-anatomi-mmlu-arena"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM benchmark&apos;larının matematiksel ve epistemik anatomi: MMLU (Hendrycks 2020 — 57 task), HumanEval (Chen 2021 — kod), MT-Bench (Zheng 2023 — chat), LMSys Chatbot Arena (community ELO ranking), GPQA (Rein 2023 — graduate-level reasoning). &apos;Niye bir benchmark yeterli değil?&apos; Türkçe için TR-MMLU, MUKAYESE, BoazıçNLP. **Benchmark contamination** sorununun ciddi analizi — model&apos;in eğitim verisinde test soruları varsa skor yanıltıcı. Holistic evaluation yaklaşımı.</image:caption>
      <image:title>Benchmark Anatomi: MMLU&apos;dan LMSys Arena&apos;ya — LLM Kalitesini Ölçmenin Bilimi ve Sanatı</image:title>
    </image:image>
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  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/benchmark-anatomi-mmlu-arena</loc>
    <lastmod>2026-05-13T13:00:32.050Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/benchmark-anatomi-mmlu-arena"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/benchmark-anatomi-mmlu-arena"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/benchmark-anatomi-mmlu-arena"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM benchmark&apos;larının matematiksel ve epistemik anatomi: MMLU (Hendrycks 2020 — 57 task), HumanEval (Chen 2021 — kod), MT-Bench (Zheng 2023 — chat), LMSys Chatbot Arena (community ELO ranking), GPQA (Rein 2023 — graduate-level reasoning). &apos;Niye bir benchmark yeterli değil?&apos; Türkçe için TR-MMLU, MUKAYESE, BoazıçNLP. **Benchmark contamination** sorununun ciddi analizi — model&apos;in eğitim verisinde test soruları varsa skor yanıltıcı. Holistic evaluation yaklaşımı.</image:caption>
      <image:title>Benchmark Anatomi: MMLU&apos;dan LMSys Arena&apos;ya — LLM Kalitesini Ölçmenin Bilimi ve Sanatı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/production-eval-framework-test-set-llm-judge</loc>
    <lastmod>2026-05-13T13:00:32.142Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/production-eval-framework-test-set-llm-judge"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/production-eval-framework-test-set-llm-judge"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/production-eval-framework-test-set-llm-judge"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Production-grade LLM evaluation framework kurmak: test set design (sampling strategy, edge cases, adversarial), automated eval pipeline (pytest-like setup), LLM-as-a-judge stratejileri (GPT-4o vs Claude vs ensemble, bias detection), error analysis (clustering, root cause), A/B testing protokolleri (statistical significance, sample size). Modül 15-20&apos;deki 7 production artefakt&apos;ı objektif karşılaştırma. Python + Pydantic ile clean evaluation code.</image:caption>
      <image:title>Production Evaluation Framework: Test Set Design&apos;dan LLM-as-Judge&apos;a — Kendi Türkçe Eval Sistemi Kur</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/production-eval-framework-test-set-llm-judge</loc>
    <lastmod>2026-05-13T13:00:32.142Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/production-eval-framework-test-set-llm-judge"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/production-eval-framework-test-set-llm-judge"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/production-eval-framework-test-set-llm-judge"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Production-grade LLM evaluation framework kurmak: test set design (sampling strategy, edge cases, adversarial), automated eval pipeline (pytest-like setup), LLM-as-a-judge stratejileri (GPT-4o vs Claude vs ensemble, bias detection), error analysis (clustering, root cause), A/B testing protokolleri (statistical significance, sample size). Modül 15-20&apos;deki 7 production artefakt&apos;ı objektif karşılaştırma. Python + Pydantic ile clean evaluation code.</image:caption>
      <image:title>Production Evaluation Framework: Test Set Design&apos;dan LLM-as-Judge&apos;a — Kendi Türkçe Eval Sistemi Kur</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-tr-llmarena-leaderboard</loc>
    <lastmod>2026-05-13T13:00:32.233Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-tr-llmarena-leaderboard"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-tr-llmarena-leaderboard"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-tr-llmarena-leaderboard"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 21 capstone: Türkçe LMSys benzeri community-driven leaderboard. Çift-anonim A/B vote sistemi, ELO ranking, aylık leaderboard. HuggingFace Spaces deploy, GPT-4o/Claude/Llama-3 vs Türkçe modeller (Modül 14-20 capstone&apos;ları). Türkçe AI ekosistemine somut bilim katkısı. Müfredatın 12. production artefaktı.</image:caption>
      <image:title>Capstone Modül 21: TR-LLMArena — Türkçe LMSys-tarzı Community Leaderboard</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-tr-llmarena-leaderboard</loc>
    <lastmod>2026-05-13T13:00:32.233Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-tr-llmarena-leaderboard"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-tr-llmarena-leaderboard"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-tr-llmarena-leaderboard"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 21 capstone: Türkçe LMSys benzeri community-driven leaderboard. Çift-anonim A/B vote sistemi, ELO ranking, aylık leaderboard. HuggingFace Spaces deploy, GPT-4o/Claude/Llama-3 vs Türkçe modeller (Modül 14-20 capstone&apos;ları). Türkçe AI ekosistemine somut bilim katkısı. Müfredatın 12. production artefaktı.</image:caption>
      <image:title>Capstone Modül 21: TR-LLMArena — Türkçe LMSys-tarzı Community Leaderboard</image:title>
    </image:image>
  </url>
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    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/jailbreak-red-teaming-constitutional-ai</loc>
    <lastmod>2026-05-13T13:00:32.316Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/jailbreak-red-teaming-constitutional-ai"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/jailbreak-red-teaming-constitutional-ai"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/jailbreak-red-teaming-constitutional-ai"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM güvenliğinin saldırı + savunma tarafı: prompt injection, jailbreak teknikleri (DAN, roleplay, encoding attacks), token smuggling, indirect injection (RAG&apos;lerden sızıntı). Bai vd. 2022 Constitutional AI yaklaşımı — Anthropic&apos;in savunma stratejisi. Red-teaming protocols (OpenAI, Anthropic best practices). Türkçe-özgül jailbreak örnekleri (İslami hassasiyet bypass, KVKK bypass denemeleri). Production-grade savunma katmanları: input filter + output filter + monitoring.</image:caption>
      <image:title>Jailbreak ve Red-Teaming: &apos;DAN&apos;dan Constitutional AI&apos;a — LLM Saldırı ve Savunma Sanatı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/jailbreak-red-teaming-constitutional-ai</loc>
    <lastmod>2026-05-13T13:00:32.316Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/jailbreak-red-teaming-constitutional-ai"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/jailbreak-red-teaming-constitutional-ai"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/jailbreak-red-teaming-constitutional-ai"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>LLM güvenliğinin saldırı + savunma tarafı: prompt injection, jailbreak teknikleri (DAN, roleplay, encoding attacks), token smuggling, indirect injection (RAG&apos;lerden sızıntı). Bai vd. 2022 Constitutional AI yaklaşımı — Anthropic&apos;in savunma stratejisi. Red-teaming protocols (OpenAI, Anthropic best practices). Türkçe-özgül jailbreak örnekleri (İslami hassasiyet bypass, KVKK bypass denemeleri). Production-grade savunma katmanları: input filter + output filter + monitoring.</image:caption>
      <image:title>Jailbreak ve Red-Teaming: &apos;DAN&apos;dan Constitutional AI&apos;a — LLM Saldırı ve Savunma Sanatı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/kvkk-ab-ai-act-turkce-llm-regulasyon</loc>
    <lastmod>2026-05-13T13:00:32.405Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/kvkk-ab-ai-act-turkce-llm-regulasyon"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/kvkk-ab-ai-act-turkce-llm-regulasyon"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/kvkk-ab-ai-act-turkce-llm-regulasyon"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Türkçe LLM mühendisinin regülasyon rehberi: KVKK (6698 sayılı kanun) tüm relevant maddeler, **AB AI Act** (Haziran 2024) risk kategorileri (yasak, yüksek-risk, sınırlı, minimal), Türk şirketin AB&apos;ye hizmet verme ikilemi (hem KVKK hem AI Act compliance). Production compliance pipeline: VERBİS kaydı, veri envanteri, GDPR-uyumlu logging, KVK kurulu denetimi, AI Act high-risk dokumentasyon. Gerçek davalar ve cezalar (KVKK ile $50K+ fines).</image:caption>
      <image:title>KVKK + AB AI Act Regülasyon: Türk LLM Mühendisinin Hukuki Rehberi — Compliance Pipeline Kurmak</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/kvkk-ab-ai-act-turkce-llm-regulasyon</loc>
    <lastmod>2026-05-13T13:00:32.405Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/kvkk-ab-ai-act-turkce-llm-regulasyon"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/kvkk-ab-ai-act-turkce-llm-regulasyon"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/kvkk-ab-ai-act-turkce-llm-regulasyon"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1633332755192-727a05c4013d?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Türkçe LLM mühendisinin regülasyon rehberi: KVKK (6698 sayılı kanun) tüm relevant maddeler, **AB AI Act** (Haziran 2024) risk kategorileri (yasak, yüksek-risk, sınırlı, minimal), Türk şirketin AB&apos;ye hizmet verme ikilemi (hem KVKK hem AI Act compliance). Production compliance pipeline: VERBİS kaydı, veri envanteri, GDPR-uyumlu logging, KVK kurulu denetimi, AI Act high-risk dokumentasyon. Gerçek davalar ve cezalar (KVKK ile $50K+ fines).</image:caption>
      <image:title>KVKK + AB AI Act Regülasyon: Türk LLM Mühendisinin Hukuki Rehberi — Compliance Pipeline Kurmak</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-llm-compliance-stack</loc>
    <lastmod>2026-05-13T13:00:32.497Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-llm-compliance-stack"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-llm-compliance-stack"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-llm-compliance-stack"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 22 capstone: müfredatın 12 production artefakt&apos;ını (Modül 6-21) KVKK + AB AI Act uyumlu hale getirmek. Audit log infrastructure + encryption + deletion endpoint + breach response plan + AB temsilci + AI Act risk değerlendirme dokümantasyonu. Müfredatın **13. ve final production artefaktı**. Aynı zamanda müfredatın **resmi kapanışı** — sıfırdan AI mühendisliğine 200+ saatlik uzman seviye yolculuğun sonu.</image:caption>
      <image:title>Capstone Modül 22: Türkçe LLM Compliance Stack — Müfredatın Kapanış Kurdelesi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-llm-compliance-stack</loc>
    <lastmod>2026-05-13T13:00:32.497Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-llm-compliance-stack"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/llm-muhendisligi/capstone-turkce-llm-compliance-stack"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/llm-muhendisligi/capstone-turkce-llm-compliance-stack"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 22 capstone: müfredatın 12 production artefakt&apos;ını (Modül 6-21) KVKK + AB AI Act uyumlu hale getirmek. Audit log infrastructure + encryption + deletion endpoint + breach response plan + AB temsilci + AI Act risk değerlendirme dokümantasyonu. Müfredatın **13. ve final production artefaktı**. Aynı zamanda müfredatın **resmi kapanışı** — sıfırdan AI mühendisliğine 200+ saatlik uzman seviye yolculuğun sonu.</image:caption>
      <image:title>Capstone Modül 22: Türkçe LLM Compliance Stack — Müfredatın Kapanış Kurdelesi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/class-imbalance-problemi-accuracy-yalan</loc>
    <lastmod>2026-05-13T13:04:49.092Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/class-imbalance-problemi-accuracy-yalan"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/class-imbalance-problemi-accuracy-yalan"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/class-imbalance-problemi-accuracy-yalan"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1559526324-4b87b5e36e44?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anomaly detection&apos;ın temel zorluğu: dengesiz sınıf dağılımı. 1:1.000.000 oranlarında neden klasik ML çöker, accuracy paradoksu, imbalanced learning&apos;in matematiksel ve pratik etkileri, sektörel imbalance tablosu.</image:caption>
      <image:title>Class Imbalance Problemi: 1:1.000.000 Oranında Fraud ve Neden Accuracy Yalan Söyler</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/class-imbalance-problemi-accuracy-yalan</loc>
    <lastmod>2026-05-13T13:04:49.092Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/class-imbalance-problemi-accuracy-yalan"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/class-imbalance-problemi-accuracy-yalan"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/class-imbalance-problemi-accuracy-yalan"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1559526324-4b87b5e36e44?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Anomaly detection&apos;ın temel zorluğu: dengesiz sınıf dağılımı. 1:1.000.000 oranlarında neden klasik ML çöker, accuracy paradoksu, imbalanced learning&apos;in matematiksel ve pratik etkileri, sektörel imbalance tablosu.</image:caption>
      <image:title>Class Imbalance Problemi: 1:1.000.000 Oranında Fraud ve Neden Accuracy Yalan Söyler</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/sampling-stratejileri-smote-adasyn</loc>
    <lastmod>2026-05-13T13:04:49.181Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/sampling-stratejileri-smote-adasyn"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/sampling-stratejileri-smote-adasyn"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/sampling-stratejileri-smote-adasyn"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1543286386-713bdd548da4?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Imbalanced veride sentetik pozitif örnek üretme: Random oversampling/undersampling, SMOTE, ADASYN, Borderline-SMOTE, SMOTE-NC (numeric + categorical), SMOTE-Tomek hibrit; imblearn pipeline&apos;ı ve sık karşılaşılan tuzaklar.</image:caption>
      <image:title>Sampling Stratejileri: SMOTE, ADASYN, Borderline-SMOTE, SMOTE-NC — Sentetik Pozitif Üretmenin Sanatı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/sampling-stratejileri-smote-adasyn</loc>
    <lastmod>2026-05-13T13:04:49.181Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/sampling-stratejileri-smote-adasyn"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/sampling-stratejileri-smote-adasyn"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/sampling-stratejileri-smote-adasyn"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1543286386-713bdd548da4?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Imbalanced veride sentetik pozitif örnek üretme: Random oversampling/undersampling, SMOTE, ADASYN, Borderline-SMOTE, SMOTE-NC (numeric + categorical), SMOTE-Tomek hibrit; imblearn pipeline&apos;ı ve sık karşılaşılan tuzaklar.</image:caption>
      <image:title>Sampling Stratejileri: SMOTE, ADASYN, Borderline-SMOTE, SMOTE-NC — Sentetik Pozitif Üretmenin Sanatı</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/cost-sensitive-learning-focal-loss</loc>
    <lastmod>2026-05-13T13:04:49.268Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/cost-sensitive-learning-focal-loss"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/cost-sensitive-learning-focal-loss"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/cost-sensitive-learning-focal-loss"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sampling alternatifi: loss function&apos;ı değiştirme. Cost matrix, class weight, sample weight, asymmetric loss, focal loss (Lin et al., 2017), Tversky loss, ve imbalanced AD&apos;de pratik uygulamalar.</image:caption>
      <image:title>Cost-Sensitive Learning ve Focal Loss: Loss Function&apos;ı Imbalanced&apos;a Eğitmek</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/cost-sensitive-learning-focal-loss</loc>
    <lastmod>2026-05-13T13:04:49.268Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/cost-sensitive-learning-focal-loss"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/cost-sensitive-learning-focal-loss"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/cost-sensitive-learning-focal-loss"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Sampling alternatifi: loss function&apos;ı değiştirme. Cost matrix, class weight, sample weight, asymmetric loss, focal loss (Lin et al., 2017), Tversky loss, ve imbalanced AD&apos;de pratik uygulamalar.</image:caption>
      <image:title>Cost-Sensitive Learning ve Focal Loss: Loss Function&apos;ı Imbalanced&apos;a Eğitmek</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/weak-supervision-snorkel-programmatic-labeling</loc>
    <lastmod>2026-05-13T13:04:49.355Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/weak-supervision-snorkel-programmatic-labeling"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/weak-supervision-snorkel-programmatic-labeling"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/weak-supervision-snorkel-programmatic-labeling"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Manuel etiket pahalı olduğunda &apos;programmatic labeling&apos;: Snorkel framework, labeling functions, label model (generative), Cleanlab ile etiket düzeltme, weak supervision&apos;ın güçlü ve zayıf yönleri.</image:caption>
      <image:title>Weak Supervision ve Snorkel: Etiket Pahalı Olduğunda Programmatic Labeling</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/weak-supervision-snorkel-programmatic-labeling</loc>
    <lastmod>2026-05-13T13:04:49.355Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/weak-supervision-snorkel-programmatic-labeling"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/weak-supervision-snorkel-programmatic-labeling"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/weak-supervision-snorkel-programmatic-labeling"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1620712943543-bcc4688e7485?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Manuel etiket pahalı olduğunda &apos;programmatic labeling&apos;: Snorkel framework, labeling functions, label model (generative), Cleanlab ile etiket düzeltme, weak supervision&apos;ın güçlü ve zayıf yönleri.</image:caption>
      <image:title>Weak Supervision ve Snorkel: Etiket Pahalı Olduğunda Programmatic Labeling</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/anomali-tespiti/hands-on-ieee-cis-fraud-4-sampling-benchmark</loc>
    <lastmod>2026-05-13T13:08:25.087Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/anomali-tespiti/hands-on-ieee-cis-fraud-4-sampling-benchmark"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/anomali-tespiti/hands-on-ieee-cis-fraud-4-sampling-benchmark"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/anomali-tespiti/hands-on-ieee-cis-fraud-4-sampling-benchmark"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1554224155-6726b3ff858f?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Kaggle IEEE-CIS Fraud Kaggle verisinde 4 imbalance stratejisi (baseline / SMOTE / class_weight / focal loss) yan yana koşturulup PR-AUC, recall@k ve maliyet karşılaştırması — Capstone 1&apos;in temel taşı.</image:caption>
      <image:title>Hands-on Lab: IEEE-CIS Fraud Verisinde 4 Sampling Stratejisi Benchmark</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/anomali-tespiti/hands-on-ieee-cis-fraud-4-sampling-benchmark</loc>
    <lastmod>2026-05-13T13:08:25.087Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
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      <image:caption>Tavsiye motorları interneti şekillendiren tek mühendislik disiplinidir: Netflix izlemelerinin %80&apos;i, YouTube tüketiminin %70&apos;i, Amazon&apos;un %35&apos;i öneri sistemlerinden gelir. Bu disiplinin doğuşunu, milyar dolarlık etkisini ve neden tam şimdi öğrenilmesi gerektiğini görüyoruz.</image:caption>
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      <image:caption>Bu kurs neden klasik &apos;Coursera tarzından&apos; farklı? Her konuyu 5 aşamada işliyoruz: (1) Kağıt-kalem matematik, (2) Saf NumPy ile sıfırdan, (3) Kütüphane ile üretim-tarzı, (4) Aynı dataset üzerinde benchmark, (5) &apos;production gotcha&apos; notu. Bu sıra neden 3x daha derin bir öğrenme verir?</image:caption>
      <image:title>Kurs Felsefesi: Matematik → Manuel Kod → Kütüphane → Benchmark → Üretim</image:title>
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      <image:caption>Kurs boyunca kullanacağımız 8 datasetin tam profili: MovieLens (3 boyut), Amazon Reviews (2023), RetailRocket, H&amp;M Fashion, MIND News, Spotify MPD, Last.fm, Yelp. Her birinin lisansı, boyutu, indirme adımı, neye uygun olduğu ve etik sözleşme.</image:caption>
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      <image:caption>Kurs boyunca kullanacağımız 8 datasetin tam profili: MovieLens (3 boyut), Amazon Reviews (2023), RetailRocket, H&amp;M Fashion, MIND News, Spotify MPD, Last.fm, Yelp. Her birinin lisansı, boyutu, indirme adımı, neye uygun olduğu ve etik sözleşme.</image:caption>
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      <image:caption>6 büyük şirketin yayınlanmış mühendislik blog&apos;larına dayalı somut mimari turu: Netflix retrieval-ranking pipeline&apos;ı, YouTube&apos;un 2 aşamalı modeli, Spotify Discover Weekly&apos;nin BaRT mimarisi, Amazon item-CF mirası, TikTok Monolith, Trendyol kişiselleştirme.</image:caption>
      <image:title>Tavsiye Motorları Nerede Çalışır? Netflix, YouTube, Spotify, Amazon, TikTok, Trendyol Mimari Turu</image:title>
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      <image:caption>Bir recommender problem 4 farklı şekilde formüle edilebilir — ve doğru formülasyonu seçmek çoğu zaman doğru algoritmayı seçmekten önemlidir. Her birinin matematiksel tanımı, ne zaman seçilir, hangi metrikle ölçülür ve hangi gerçek senaryolar yaşar.</image:caption>
      <image:title>Problem Tipolojisi: Rating Prediction vs. Ranking vs. Top-N Retrieval vs. Sequential</image:title>
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    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/problem-tipolojisi-rating-ranking-topn-sequential"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1556740772-1a741367b93e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir recommender problem 4 farklı şekilde formüle edilebilir — ve doğru formülasyonu seçmek çoğu zaman doğru algoritmayı seçmekten önemlidir. Her birinin matematiksel tanımı, ne zaman seçilir, hangi metrikle ölçülür ve hangi gerçek senaryolar yaşar.</image:caption>
      <image:title>Problem Tipolojisi: Rating Prediction vs. Ranking vs. Top-N Retrieval vs. Sequential</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/explicit-implicit-feedback-rating-click-skip</loc>
    <lastmod>2026-05-13T13:29:33.622Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/explicit-implicit-feedback-rating-click-skip"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/explicit-implicit-feedback-rating-click-skip"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/explicit-implicit-feedback-rating-click-skip"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Recommender&apos;da kullanılan iki temel veri türü: explicit (kullanıcı bilerek verdiği yıldız/like) vs implicit (click, dwell time, completion, skip). Farkları, loss fonksiyonu etkisi, bias kaynakları, hibrit kullanımı ve gerçek e-ticaret etiketleme stratejileri.</image:caption>
      <image:title>Explicit ve Implicit Feedback: 1-5 Yıldızdan Tıklama-Skip Davranışına Eksiksiz Rehber</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/explicit-implicit-feedback-rating-click-skip</loc>
    <lastmod>2026-05-13T13:29:33.622Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/explicit-implicit-feedback-rating-click-skip"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/explicit-implicit-feedback-rating-click-skip"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/explicit-implicit-feedback-rating-click-skip"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Recommender&apos;da kullanılan iki temel veri türü: explicit (kullanıcı bilerek verdiği yıldız/like) vs implicit (click, dwell time, completion, skip). Farkları, loss fonksiyonu etkisi, bias kaynakları, hibrit kullanımı ve gerçek e-ticaret etiketleme stratejileri.</image:caption>
      <image:title>Explicit ve Implicit Feedback: 1-5 Yıldızdan Tıklama-Skip Davranışına Eksiksiz Rehber</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/cold-start-problemi-user-item-system-cozumler</loc>
    <lastmod>2026-05-13T13:29:33.716Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/cold-start-problemi-user-item-system-cozumler"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/cold-start-problemi-user-item-system-cozumler"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/cold-start-problemi-user-item-system-cozumler"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1607083206869-4c7672e72a8a?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Recommender sistemlerin en sinir bozucu problemi: hakkında veri olmayan kullanıcı/item&apos;a nasıl öneri yaparsın? User cold-start, item cold-start ve system cold-start için pratik strateji haritası — Netflix&apos;in 5-film ekranından TikTok&apos;un viral-loop&apos;una.</image:caption>
      <image:title>Cold-Start Probleminin Üç Yüzü: User, Item, System — ve Pratik Çözümler</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/cold-start-problemi-user-item-system-cozumler</loc>
    <lastmod>2026-05-13T13:29:33.716Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/cold-start-problemi-user-item-system-cozumler"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/cold-start-problemi-user-item-system-cozumler"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/cold-start-problemi-user-item-system-cozumler"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1607083206869-4c7672e72a8a?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Recommender sistemlerin en sinir bozucu problemi: hakkında veri olmayan kullanıcı/item&apos;a nasıl öneri yaparsın? User cold-start, item cold-start ve system cold-start için pratik strateji haritası — Netflix&apos;in 5-film ekranından TikTok&apos;un viral-loop&apos;una.</image:caption>
      <image:title>Cold-Start Probleminin Üç Yüzü: User, Item, System — ve Pratik Çözümler</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/movielens-schema-eda-polars-yukleme</loc>
    <lastmod>2026-05-13T13:29:33.819Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/movielens-schema-eda-polars-yukleme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/movielens-schema-eda-polars-yukleme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/movielens-schema-eda-polars-yukleme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>MovieLens-100K, 1M ve 25M&apos;in dosya yapısı, satır-satır schema, Polars ile lazy/streaming load (Pandas&apos;tan 10-30x hızlı), sparse matrix&apos;e çevirme, ilk EDA grafikleri ve veri kalite kontrolleri.</image:caption>
      <image:title>MovieLens&apos;i Sıfırdan Tanıyalım: Schema, EDA ve Polars ile Verimli Yükleme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/movielens-schema-eda-polars-yukleme</loc>
    <lastmod>2026-05-13T13:29:33.819Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/movielens-schema-eda-polars-yukleme"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/movielens-schema-eda-polars-yukleme"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/movielens-schema-eda-polars-yukleme"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>MovieLens-100K, 1M ve 25M&apos;in dosya yapısı, satır-satır schema, Polars ile lazy/streaming load (Pandas&apos;tan 10-30x hızlı), sparse matrix&apos;e çevirme, ilk EDA grafikleri ve veri kalite kontrolleri.</image:caption>
      <image:title>MovieLens&apos;i Sıfırdan Tanıyalım: Schema, EDA ve Polars ile Verimli Yükleme</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/implicit-feedback-etikete-cevirmek-click-dwell-aggregation</loc>
    <lastmod>2026-05-13T13:29:33.917Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/implicit-feedback-etikete-cevirmek-click-dwell-aggregation"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/implicit-feedback-etikete-cevirmek-click-dwell-aggregation"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/implicit-feedback-etikete-cevirmek-click-dwell-aggregation"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir e-ticaret site log&apos;unun ham hali → modelin eğitilebileceği etiket veri seti. Hu/Koren confidence weighting&apos;in matematiği ve NumPy implementasyonu, multi-signal weighted aggregation, session reconstruction, label leakage&apos;ı önleme.</image:caption>
      <image:title>Implicit Feedback&apos;i Etikete Çevirmek: Click, Dwell ve Multi-Signal Aggregation</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/implicit-feedback-etikete-cevirmek-click-dwell-aggregation</loc>
    <lastmod>2026-05-13T13:29:33.917Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/implicit-feedback-etikete-cevirmek-click-dwell-aggregation"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/implicit-feedback-etikete-cevirmek-click-dwell-aggregation"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/implicit-feedback-etikete-cevirmek-click-dwell-aggregation"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir e-ticaret site log&apos;unun ham hali → modelin eğitilebileceği etiket veri seti. Hu/Koren confidence weighting&apos;in matematiği ve NumPy implementasyonu, multi-signal weighted aggregation, session reconstruction, label leakage&apos;ı önleme.</image:caption>
      <image:title>Implicit Feedback&apos;i Etikete Çevirmek: Click, Dwell ve Multi-Signal Aggregation</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/bias-galaksisi-position-popularity-ips-correction</loc>
    <lastmod>2026-05-13T13:29:34.005Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/bias-galaksisi-position-popularity-ips-correction"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/bias-galaksisi-position-popularity-ips-correction"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/bias-galaksisi-position-popularity-ips-correction"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Recommender sistemlerinin 5 önemli bias&apos;ı (position, presentation, popularity, exposure, selection), her birinin matematiksel tanımı, log data&apos;da gözlemleme yolları, ve Inverse Propensity Scoring (IPS) düzeltmesinin türetimi + NumPy implementasyonu.</image:caption>
      <image:title>Bias Galaksisi: Position, Presentation, Popularity ve IPS Düzeltmesi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/bias-galaksisi-position-popularity-ips-correction</loc>
    <lastmod>2026-05-13T13:29:34.005Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/bias-galaksisi-position-popularity-ips-correction"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/bias-galaksisi-position-popularity-ips-correction"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/bias-galaksisi-position-popularity-ips-correction"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Recommender sistemlerinin 5 önemli bias&apos;ı (position, presentation, popularity, exposure, selection), her birinin matematiksel tanımı, log data&apos;da gözlemleme yolları, ve Inverse Propensity Scoring (IPS) düzeltmesinin türetimi + NumPy implementasyonu.</image:caption>
      <image:title>Bias Galaksisi: Position, Presentation, Popularity ve IPS Düzeltmesi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/gdpr-kvkk-unutulma-hakki-recommender-compliance</loc>
    <lastmod>2026-05-13T13:29:34.096Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/gdpr-kvkk-unutulma-hakki-recommender-compliance"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/gdpr-kvkk-unutulma-hakki-recommender-compliance"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/gdpr-kvkk-unutulma-hakki-recommender-compliance"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1556740772-1a741367b93e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir recommender sistemi data subject rights (erişim, silme, taşıma) ile nasıl uyum sağlar? AB AI Act 2024-2026 takvimi, KVKK&apos;nın 2025 güncellemesi, ML modelinden user data&apos;sı çıkarma teknikleri (machine unlearning), audit log gereksinimleri.</image:caption>
      <image:title>GDPR, KVKK ve Unutulma Hakkı: Recommender&apos;da Hukuk Uyumu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/gdpr-kvkk-unutulma-hakki-recommender-compliance</loc>
    <lastmod>2026-05-13T13:29:34.096Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/gdpr-kvkk-unutulma-hakki-recommender-compliance"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/gdpr-kvkk-unutulma-hakki-recommender-compliance"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/gdpr-kvkk-unutulma-hakki-recommender-compliance"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1556740772-1a741367b93e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bir recommender sistemi data subject rights (erişim, silme, taşıma) ile nasıl uyum sağlar? AB AI Act 2024-2026 takvimi, KVKK&apos;nın 2025 güncellemesi, ML modelinden user data&apos;sı çıkarma teknikleri (machine unlearning), audit log gereksinimleri.</image:caption>
      <image:title>GDPR, KVKK ve Unutulma Hakkı: Recommender&apos;da Hukuk Uyumu</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/dogruluk-metrikleri-rmse-ndcg-map-numpy</loc>
    <lastmod>2026-05-13T13:29:34.186Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/dogruluk-metrikleri-rmse-ndcg-map-numpy"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/dogruluk-metrikleri-rmse-ndcg-map-numpy"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/dogruluk-metrikleri-rmse-ndcg-map-numpy"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>8 ana doğruluk metriğinin tam matematiksel tanımı, sıfırdan NumPy implementasyonu, MovieLens üzerinde karşılaştırmalı çalıştırma, ve hangi durumda hangi metriği seçmen gerektiği — recommender mühendisinin metric cheat sheet&apos;i.</image:caption>
      <image:title>Doğruluk Metrikleri: RMSE, MAE, Precision@K, Recall@K, MAP, MRR, NDCG, HR@K — Tam Matematik + NumPy</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/dogruluk-metrikleri-rmse-ndcg-map-numpy</loc>
    <lastmod>2026-05-13T13:29:34.186Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/dogruluk-metrikleri-rmse-ndcg-map-numpy"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/dogruluk-metrikleri-rmse-ndcg-map-numpy"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/dogruluk-metrikleri-rmse-ndcg-map-numpy"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>8 ana doğruluk metriğinin tam matematiksel tanımı, sıfırdan NumPy implementasyonu, MovieLens üzerinde karşılaştırmalı çalıştırma, ve hangi durumda hangi metriği seçmen gerektiği — recommender mühendisinin metric cheat sheet&apos;i.</image:caption>
      <image:title>Doğruluk Metrikleri: RMSE, MAE, Precision@K, Recall@K, MAP, MRR, NDCG, HR@K — Tam Matematik + NumPy</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/beyond-accuracy-coverage-diversity-novelty-serendipity</loc>
    <lastmod>2026-05-13T13:29:34.294Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/beyond-accuracy-coverage-diversity-novelty-serendipity"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/beyond-accuracy-coverage-diversity-novelty-serendipity"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/beyond-accuracy-coverage-diversity-novelty-serendipity"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>NDCG&apos;si yüksek ama kullanıcı sıkılan recommender&apos;ın sebebi: tek metrik &apos;doğruluk&apos; ile ölçüldü. Coverage, intra-list similarity (ILS), novelty, serendipity ve gini coefficient ile sistemin tüm yüzlerini ölç.</image:caption>
      <image:title>Beyond-Accuracy: Coverage, Diversity (ILS), Novelty, Serendipity ve Popularity Bias Ölçümü</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/beyond-accuracy-coverage-diversity-novelty-serendipity</loc>
    <lastmod>2026-05-13T13:29:34.294Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/beyond-accuracy-coverage-diversity-novelty-serendipity"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/beyond-accuracy-coverage-diversity-novelty-serendipity"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/beyond-accuracy-coverage-diversity-novelty-serendipity"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>NDCG&apos;si yüksek ama kullanıcı sıkılan recommender&apos;ın sebebi: tek metrik &apos;doğruluk&apos; ile ölçüldü. Coverage, intra-list similarity (ILS), novelty, serendipity ve gini coefficient ile sistemin tüm yüzlerini ölç.</image:caption>
      <image:title>Beyond-Accuracy: Coverage, Diversity (ILS), Novelty, Serendipity ve Popularity Bias Ölçümü</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/veri-bolme-stratejileri-random-time-user-loo</loc>
    <lastmod>2026-05-13T13:29:34.393Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/veri-bolme-stratejileri-random-time-user-loo"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/veri-bolme-stratejileri-random-time-user-loo"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/veri-bolme-stratejileri-random-time-user-loo"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>MovieLens&apos;i nasıl bölersen NDCG&apos;nin değişir — 0.15 veya 0.25. Bu derste 5 ana split stratejisi, her birinin ne zaman doğru, ne zaman &apos;leakage&apos; verdiği ve production realism açısından karşılaştırması.</image:caption>
      <image:title>Veri Bölme Stratejileri: Random, Time, User, Leave-One-Out — Pratik Trade-Off</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/veri-bolme-stratejileri-random-time-user-loo</loc>
    <lastmod>2026-05-13T13:29:34.393Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/veri-bolme-stratejileri-random-time-user-loo"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/veri-bolme-stratejileri-random-time-user-loo"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/veri-bolme-stratejileri-random-time-user-loo"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>MovieLens&apos;i nasıl bölersen NDCG&apos;nin değişir — 0.15 veya 0.25. Bu derste 5 ana split stratejisi, her birinin ne zaman doğru, ne zaman &apos;leakage&apos; verdiği ve production realism açısından karşılaştırması.</image:caption>
      <image:title>Veri Bölme Stratejileri: Random, Time, User, Leave-One-Out — Pratik Trade-Off</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/online-evaluation-ab-test-interleaving-cuped</loc>
    <lastmod>2026-05-13T13:29:34.482Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/online-evaluation-ab-test-interleaving-cuped"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/online-evaluation-ab-test-interleaving-cuped"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/online-evaluation-ab-test-interleaving-cuped"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Offline NDCG +%2 artırdın — production&apos;a deploy etmeden önce A/B test ile gerçekten kullanıcının davranışını değiştiriyor mu doğrula. A/B test sample size matematiği, interleaving (10x daha verimli), CUPED varyans azaltma ve switchback testing.</image:caption>
      <image:title>Online Evaluation: A/B Test, Interleaving, CUPED ve İstatistiksel Güç</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/online-evaluation-ab-test-interleaving-cuped</loc>
    <lastmod>2026-05-13T13:29:34.482Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/online-evaluation-ab-test-interleaving-cuped"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/online-evaluation-ab-test-interleaving-cuped"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/online-evaluation-ab-test-interleaving-cuped"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Offline NDCG +%2 artırdın — production&apos;a deploy etmeden önce A/B test ile gerçekten kullanıcının davranışını değiştiriyor mu doğrula. A/B test sample size matematiği, interleaving (10x daha verimli), CUPED varyans azaltma ve switchback testing.</image:caption>
      <image:title>Online Evaluation: A/B Test, Interleaving, CUPED ve İstatistiksel Güç</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/offline-online-bosluk-dacrema-krizi-protokol</loc>
    <lastmod>2026-05-13T13:29:34.571Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/offline-online-bosluk-dacrema-krizi-protokol"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/offline-online-bosluk-dacrema-krizi-protokol"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/offline-online-bosluk-dacrema-krizi-protokol"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1556742502-ec7c0e9f34b1?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>2019&apos;da Dacrema, Cremonesi, Jannach paper&apos;ı recommender literatürünü silkeledi: &apos;Neural recommender&apos;lar gerçekten iyi mi? Çoğunu klasik k-NN bile geçiyor.&apos; Bu derste reproducibility krizi, offline-online korelasyon problemi ve nasıl doğru protokol seçileceği.</image:caption>
      <image:title>Offline-Online Boşluğu: Dacrema Krizi ve Doğru Protokol Seçimi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/offline-online-bosluk-dacrema-krizi-protokol</loc>
    <lastmod>2026-05-13T13:29:34.571Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/offline-online-bosluk-dacrema-krizi-protokol"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/offline-online-bosluk-dacrema-krizi-protokol"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/offline-online-bosluk-dacrema-krizi-protokol"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1556742502-ec7c0e9f34b1?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>2019&apos;da Dacrema, Cremonesi, Jannach paper&apos;ı recommender literatürünü silkeledi: &apos;Neural recommender&apos;lar gerçekten iyi mi? Çoğunu klasik k-NN bile geçiyor.&apos; Bu derste reproducibility krizi, offline-online korelasyon problemi ve nasıl doğru protokol seçileceği.</image:caption>
      <image:title>Offline-Online Boşluğu: Dacrema Krizi ve Doğru Protokol Seçimi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-mulakat-sureci-hazirlik-stratejisi</loc>
    <lastmod>2026-05-13T13:16:01.386Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-mulakat-sureci-hazirlik-stratejisi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/ai-mulakat-sureci-hazirlik-stratejisi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-mulakat-sureci-hazirlik-stratejisi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&amp;q=80</image:loc>
      <image:caption>AI/ML mühendisi pozisyonlarına Türkiye&apos;de hazırlanmanın uçtan uca rehberi: pazar gerçekleri (2026 maaş aralıkları), şirket bazlı mülakat akışları (Trendyol, Getir, Hepsiburada, bankacılık, FAANG remote), 8 haftalık hazırlık planı, CV optimizasyonu, pre-screening tuzakları, LinkedIn outreach stratejisi ve yurt dışı remote pozisyonlara nasıl başvurulur.</image:caption>
      <image:title>AI Mülakat Süreci &amp; Hazırlık Stratejisi — Türkiye Pazarı 2026</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/ai-mulakat-sureci-hazirlik-stratejisi</loc>
    <lastmod>2026-05-13T13:16:01.386Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-mulakat-sureci-hazirlik-stratejisi"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/ai-mulakat-sureci-hazirlik-stratejisi"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-mulakat-sureci-hazirlik-stratejisi"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&amp;q=80</image:loc>
      <image:caption>AI/ML mühendisi pozisyonlarına Türkiye&apos;de hazırlanmanın uçtan uca rehberi: pazar gerçekleri (2026 maaş aralıkları), şirket bazlı mülakat akışları (Trendyol, Getir, Hepsiburada, bankacılık, FAANG remote), 8 haftalık hazırlık planı, CV optimizasyonu, pre-screening tuzakları, LinkedIn outreach stratejisi ve yurt dışı remote pozisyonlara nasıl başvurulur.</image:caption>
      <image:title>AI Mülakat Süreci &amp; Hazırlık Stratejisi — Türkiye Pazarı 2026</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-mulakat-50-konsept-sorusu</loc>
    <lastmod>2026-05-13T13:16:01.519Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-mulakat-50-konsept-sorusu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/ai-mulakat-50-konsept-sorusu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-mulakat-50-konsept-sorusu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&amp;q=80</image:loc>
      <image:caption>AI/ML mühendisi mülakatlarında en sık çıkan 50+ konsept sorusu, doğru cevap stratejileri, zayıf cevap tuzakları ve takip sorularına nasıl hazırlanılır. ML fundamentals, deep learning, LLM/RAG/agent, production/MLOps, güvenlik/etik ve Türkçe NLP spesifik kategorilerinde organize edilmiştir.</image:caption>
      <image:title>50+ Konsept Sorusu — Gerçek AI Mülakatlarında Çıkanlar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/ai-mulakat-50-konsept-sorusu</loc>
    <lastmod>2026-05-13T13:16:01.519Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-mulakat-50-konsept-sorusu"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/ai-mulakat-50-konsept-sorusu"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-mulakat-50-konsept-sorusu"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&amp;q=80</image:loc>
      <image:caption>AI/ML mühendisi mülakatlarında en sık çıkan 50+ konsept sorusu, doğru cevap stratejileri, zayıf cevap tuzakları ve takip sorularına nasıl hazırlanılır. ML fundamentals, deep learning, LLM/RAG/agent, production/MLOps, güvenlik/etik ve Türkçe NLP spesifik kategorilerinde organize edilmiştir.</image:caption>
      <image:title>50+ Konsept Sorusu — Gerçek AI Mülakatlarında Çıkanlar</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-mulakat-sistem-tasarim-kod-davranissal-maas</loc>
    <lastmod>2026-05-13T13:16:01.647Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-mulakat-sistem-tasarim-kod-davranissal-maas"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/ai-mulakat-sistem-tasarim-kod-davranissal-maas"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-mulakat-sistem-tasarim-kod-davranissal-maas"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&amp;q=80</image:loc>
      <image:caption>AI mülakatının pratik kısımları: 5 sistem tasarımı vakası (Türkçe RAG, recommendation, fraud detection, LLM cost optimization, multi-tenant platform), 5 kod sorusu (numpy/pandas/sklearn/PyTorch/LangChain), 10 davranışsal STAR senaryosu ve Türkiye + remote pazarı için maaş görüşmesi taktikleri.</image:caption>
      <image:title>Sistem Tasarımı + Kod + Davranışsal + Maaş Görüşmesi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/ai-mulakat-sistem-tasarim-kod-davranissal-maas</loc>
    <lastmod>2026-05-13T13:16:01.647Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-mulakat-sistem-tasarim-kod-davranissal-maas"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/yapay-zekaya-giris/ai-mulakat-sistem-tasarim-kod-davranissal-maas"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/yapay-zekaya-giris/ai-mulakat-sistem-tasarim-kod-davranissal-maas"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1200&amp;q=80</image:loc>
      <image:caption>AI mülakatının pratik kısımları: 5 sistem tasarımı vakası (Türkçe RAG, recommendation, fraud detection, LLM cost optimization, multi-tenant platform), 5 kod sorusu (numpy/pandas/sklearn/PyTorch/LangChain), 10 davranışsal STAR senaryosu ve Türkiye + remote pazarı için maaş görüşmesi taktikleri.</image:caption>
      <image:title>Sistem Tasarımı + Kod + Davranışsal + Maaş Görüşmesi</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/content-based-filtering-felsefesi-neye-benziyor</loc>
    <lastmod>2026-05-13T13:29:34.659Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/content-based-filtering-felsefesi-neye-benziyor"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/content-based-filtering-felsefesi-neye-benziyor"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/content-based-filtering-felsefesi-neye-benziyor"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Collaborative filtering &apos;benzer kullanıcılar&apos; arar, content-based &apos;benzer item&apos;lar&apos; arar. Bu felsefi fark teknik kararları belirler — cold-start avantajı, filter bubble dezavantajı, hybrid stratejileri. Konsept + matematik + endüstri pozisyonlama.</image:caption>
      <image:title>Content-Based Filtering Felsefesi: &apos;Ne İzledi&apos; Yerine &apos;Neye Benziyor&apos;</image:title>
    </image:image>
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    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/content-based-filtering-felsefesi-neye-benziyor</loc>
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    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/content-based-filtering-felsefesi-neye-benziyor"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/content-based-filtering-felsefesi-neye-benziyor"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/content-based-filtering-felsefesi-neye-benziyor"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Collaborative filtering &apos;benzer kullanıcılar&apos; arar, content-based &apos;benzer item&apos;lar&apos; arar. Bu felsefi fark teknik kararları belirler — cold-start avantajı, filter bubble dezavantajı, hybrid stratejileri. Konsept + matematik + endüstri pozisyonlama.</image:caption>
      <image:title>Content-Based Filtering Felsefesi: &apos;Ne İzledi&apos; Yerine &apos;Neye Benziyor&apos;</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/item-profilleme-tfidf-bm25-encoding</loc>
    <lastmod>2026-05-13T13:29:34.752Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/item-profilleme-tfidf-bm25-encoding"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/item-profilleme-tfidf-bm25-encoding"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/item-profilleme-tfidf-bm25-encoding"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Content-based recommender&apos;ın temeli: item&apos;ı sayısal vektöre çevirme. TF-IDF formülü tam türetim + sıfırdan NumPy implementasyon, BM25&apos;in TF-IDF&apos;ten farkı, n-gram ile film başlığı işleme, kategorik encoding (one-hot, target encoding, frequency encoding).</image:caption>
      <image:title>Item Profilleme: TF-IDF, BM25, n-gram ve Kategorik Feature Encoding — Matematik + NumPy</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/item-profilleme-tfidf-bm25-encoding</loc>
    <lastmod>2026-05-13T13:29:34.752Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/item-profilleme-tfidf-bm25-encoding"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/item-profilleme-tfidf-bm25-encoding"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/item-profilleme-tfidf-bm25-encoding"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Content-based recommender&apos;ın temeli: item&apos;ı sayısal vektöre çevirme. TF-IDF formülü tam türetim + sıfırdan NumPy implementasyon, BM25&apos;in TF-IDF&apos;ten farkı, n-gram ile film başlığı işleme, kategorik encoding (one-hot, target encoding, frequency encoding).</image:caption>
      <image:title>Item Profilleme: TF-IDF, BM25, n-gram ve Kategorik Feature Encoding — Matematik + NumPy</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/sifirdan-numpy-content-based-recommender-movielens</loc>
    <lastmod>2026-05-13T13:29:34.843Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/sifirdan-numpy-content-based-recommender-movielens"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/sifirdan-numpy-content-based-recommender-movielens"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/sifirdan-numpy-content-based-recommender-movielens"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1556740772-1a741367b93e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bu modülün omurga dersi: MovieLens-100K&apos;da gerçek bir content-based recommender kuruyoruz — sadece NumPy ile, 150 satır kod, end-to-end. Item profilleme, user profile vektörü, cosine scoring, top-N öneri, evaluation. Sonra sklearn ile karşılaştırma ve baseline tablomuza ilk satır.</image:caption>
      <image:title>Sıfırdan NumPy ile Content-Based Recommender: MovieLens-100K Üzerinde 150 Satır</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/sifirdan-numpy-content-based-recommender-movielens</loc>
    <lastmod>2026-05-13T13:29:34.843Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/sifirdan-numpy-content-based-recommender-movielens"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/sifirdan-numpy-content-based-recommender-movielens"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/sifirdan-numpy-content-based-recommender-movielens"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1556740772-1a741367b93e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Bu modülün omurga dersi: MovieLens-100K&apos;da gerçek bir content-based recommender kuruyoruz — sadece NumPy ile, 150 satır kod, end-to-end. Item profilleme, user profile vektörü, cosine scoring, top-N öneri, evaluation. Sonra sklearn ile karşılaştırma ve baseline tablomuza ilk satır.</image:caption>
      <image:title>Sıfırdan NumPy ile Content-Based Recommender: MovieLens-100K Üzerinde 150 Satır</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/production-notlari-feature-drift-multimodal-turkce-nlp</loc>
    <lastmod>2026-05-13T13:29:34.933Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/production-notlari-feature-drift-multimodal-turkce-nlp"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/production-notlari-feature-drift-multimodal-turkce-nlp"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/production-notlari-feature-drift-multimodal-turkce-nlp"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 4&apos;ün kapanışı: content-based recommender&apos;ı 6 ay production&apos;da tuttuğunda karşılaşacağın gerçek problemler. Feature distribution drift, multi-modal embedding (image+text+audio) ile cold-start gücü, CLIP/SBERT modern yaklaşımlar, Türkçe NLP özelinde stemming + BERTurk.</image:caption>
      <image:title>Production Notları: Feature Drift, Multi-Modal Content, ve Türkçe NLP&apos;nin Zorlukları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/production-notlari-feature-drift-multimodal-turkce-nlp</loc>
    <lastmod>2026-05-13T13:29:34.933Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/production-notlari-feature-drift-multimodal-turkce-nlp"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/production-notlari-feature-drift-multimodal-turkce-nlp"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/production-notlari-feature-drift-multimodal-turkce-nlp"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1635070041078-e363dbe005cb?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 4&apos;ün kapanışı: content-based recommender&apos;ı 6 ay production&apos;da tuttuğunda karşılaşacağın gerçek problemler. Feature distribution drift, multi-modal embedding (image+text+audio) ile cold-start gücü, CLIP/SBERT modern yaklaşımlar, Türkçe NLP özelinde stemming + BERTurk.</image:caption>
      <image:title>Production Notları: Feature Drift, Multi-Modal Content, ve Türkçe NLP&apos;nin Zorlukları</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/knn-cf-user-user-vs-item-item</loc>
    <lastmod>2026-05-13T13:29:35.026Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/knn-cf-user-user-vs-item-item"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/knn-cf-user-user-vs-item-item"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/knn-cf-user-user-vs-item-item"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1556740772-1a741367b93e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Recommender disiplininin doğum makalesi GroupLens 1994. 30 yıl sonra hala her recommender sistemin temel baseline&apos;ı. Bu derste user-user CF ve item-item CF&apos;in felsefi farkları, her birinin matematiksel formülasyonu, hangi senaryoda hangisinin kazandığı.</image:caption>
      <image:title>k-NN Collaborative Filtering: User-User vs Item-Item — Hangisi Ne Zaman?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/knn-cf-user-user-vs-item-item</loc>
    <lastmod>2026-05-13T13:29:35.026Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/knn-cf-user-user-vs-item-item"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/knn-cf-user-user-vs-item-item"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/knn-cf-user-user-vs-item-item"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1556740772-1a741367b93e?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Recommender disiplininin doğum makalesi GroupLens 1994. 30 yıl sonra hala her recommender sistemin temel baseline&apos;ı. Bu derste user-user CF ve item-item CF&apos;in felsefi farkları, her birinin matematiksel formülasyonu, hangi senaryoda hangisinin kazandığı.</image:caption>
      <image:title>k-NN Collaborative Filtering: User-User vs Item-Item — Hangisi Ne Zaman?</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/similarity-metrikleri-pearson-cosine-jaccard</loc>
    <lastmod>2026-05-13T13:29:35.122Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/similarity-metrikleri-pearson-cosine-jaccard"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/similarity-metrikleri-pearson-cosine-jaccard"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/similarity-metrikleri-pearson-cosine-jaccard"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tüm CF algoritmalarının temeli: 4 ana similarity metric. Pearson correlation (rating bias düzeltmesi), cosine similarity (vektör yön), adjusted cosine (user bias düzeltmesi), Jaccard (binary implicit). Tam matematiksel türetim + sıfırdan NumPy + MovieLens karşılaştırması.</image:caption>
      <image:title>Similarity Metrikleri: Pearson, Cosine, Adjusted Cosine, Jaccard — Tam Matematik + NumPy</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/similarity-metrikleri-pearson-cosine-jaccard</loc>
    <lastmod>2026-05-13T13:29:35.122Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/similarity-metrikleri-pearson-cosine-jaccard"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/similarity-metrikleri-pearson-cosine-jaccard"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/similarity-metrikleri-pearson-cosine-jaccard"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1611162617213-7d7a39e9b1d7?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Tüm CF algoritmalarının temeli: 4 ana similarity metric. Pearson correlation (rating bias düzeltmesi), cosine similarity (vektör yön), adjusted cosine (user bias düzeltmesi), Jaccard (binary implicit). Tam matematiksel türetim + sıfırdan NumPy + MovieLens karşılaştırması.</image:caption>
      <image:title>Similarity Metrikleri: Pearson, Cosine, Adjusted Cosine, Jaccard — Tam Matematik + NumPy</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/sifirdan-item-item-knn-numpy-movielens-1m</loc>
    <lastmod>2026-05-13T13:29:35.218Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/sifirdan-item-item-knn-numpy-movielens-1m"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/sifirdan-item-item-knn-numpy-movielens-1m"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/sifirdan-item-item-knn-numpy-movielens-1m"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 5&apos;in omurga dersi: MovieLens-1M üzerinde sıfırdan production-grade item-item k-NN. Adjusted cosine + shrinkage, sparse matrix optimizasyonları, offline batch precomputation pattern, top-K neighbor caching, benchmark tablomuza ikinci satır.</image:caption>
      <image:title>Sıfırdan NumPy ile Item-Item k-NN: MovieLens-1M Üzerinde Production-Grade</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/sifirdan-item-item-knn-numpy-movielens-1m</loc>
    <lastmod>2026-05-13T13:29:35.218Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/sifirdan-item-item-knn-numpy-movielens-1m"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/sifirdan-item-item-knn-numpy-movielens-1m"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/sifirdan-item-item-knn-numpy-movielens-1m"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1639762681485-074b7f938ba0?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>Modül 5&apos;in omurga dersi: MovieLens-1M üzerinde sıfırdan production-grade item-item k-NN. Adjusted cosine + shrinkage, sparse matrix optimizasyonları, offline batch precomputation pattern, top-K neighbor caching, benchmark tablomuza ikinci satır.</image:caption>
      <image:title>Sıfırdan NumPy ile Item-Item k-NN: MovieLens-1M Üzerinde Production-Grade</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/oneri-sistemleri/scalability-tavanlari-100m-rating-optimizasyon</loc>
    <lastmod>2026-05-13T13:29:35.312Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.70</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/scalability-tavanlari-100m-rating-optimizasyon"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/scalability-tavanlari-100m-rating-optimizasyon"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/scalability-tavanlari-100m-rating-optimizasyon"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>MovieLens-1M çok küçük — gerçek dünyada 100M+ rating, 10M+ item&apos;la çalışırsın. Bu derste: offline batch precomputation pattern, LSH (Locality-Sensitive Hashing), MinHash ile approximate Jaccard, MapReduce/Spark ile distributed computation, Redis-tabanlı serving.</image:caption>
      <image:title>Scalability Tavanları: 100M Rating Üstünde Optimizasyon Stratejileri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/en/learn/oneri-sistemleri/scalability-tavanlari-100m-rating-optimizasyon</loc>
    <lastmod>2026-05-13T13:29:35.312Z</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.60</priority>
    <xhtml:link rel="alternate" hreflang="tr" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/scalability-tavanlari-100m-rating-optimizasyon"/>
    <xhtml:link rel="alternate" hreflang="en" href="https://sukruyusufkaya.com/en/learn/oneri-sistemleri/scalability-tavanlari-100m-rating-optimizasyon"/>
    <xhtml:link rel="alternate" hreflang="x-default" href="https://sukruyusufkaya.com/learn/oneri-sistemleri/scalability-tavanlari-100m-rating-optimizasyon"/>
    <image:image>
      <image:loc>https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1280&amp;h=720&amp;fit=crop&amp;auto=format&amp;q=80</image:loc>
      <image:caption>MovieLens-1M çok küçük — gerçek dünyada 100M+ rating, 10M+ item&apos;la çalışırsın. Bu derste: offline batch precomputation pattern, LSH (Locality-Sensitive Hashing), MinHash ile approximate Jaccard, MapReduce/Spark ile distributed computation, Redis-tabanlı serving.</image:caption>
      <image:title>Scalability Tavanları: 100M Rating Üstünde Optimizasyon Stratejileri</image:title>
    </image:image>
  </url>
  <url>
    <loc>https://sukruyusufkaya.com/learn/path/chatgpt-ustaligi-sertifika-yolu</loc>
    <lastmod>2026-05-11T13:49:16.002Z</lastmod>
    <changefreq>weekly</changefreq>
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