Tag

#transformer

8 posts

What Is the Transformer Architecture? The Foundation of Modern AI

What is a Transformer? The Transformer architecture is an attention-based AI architecture that processes a text not word by word but all pieces at once, through their relationships to one another. This guide: a clear definition, the attention mechanism, the encoder-decoder structure, positional encoding, its link to LLM architecture, real-world examples, limits, and FAQs.

13 min

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yapay-zeka

What Is GPT? A Guide to the Generative Pre-trained Transformer

What is GPT? GPT (Generative Pre-trained Transformer) is a family of transformer-based language models pre-trained on large text data that generate text by predicting the next word. This guide: a clear definition, how GPT works, the transformer and pre-training, the autoregressive model logic, GPT vs ChatGPT, OpenAI and versions, enterprise use, limits, and FAQs.

11 min

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yapay-zeka

What Is Deep Learning? Neural Networks and Layered Architecture

What is deep learning? Deep learning is more than simply using neural networks with many layers. It is a way of learning representations from data, capturing patterns at multiple levels of abstraction, and optimizing complex decision systems end to end. This is why it has become central in computer

31 min

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yapay-zeka

What is an LLM? How Large Language Models Work — 2026 Reference

What is an LLM? How do Large Language Models (LLMs) work, what does Transformer architecture solve, what are tokens, embeddings, and context windows, and how do GPT-5, Claude Opus 4.7, Gemini 3, and Llama 4 compare? A comprehensive 2026 reference covering Turkish LLM performance, training stages, hallucination control, and cost modeling.

26 min

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yapay-zeka

What is Artificial Intelligence? A Comprehensive 2026 Guide

What is artificial intelligence? Artificial intelligence (AI) is the set of disciplines that enable machines to imitate human-like learning, reasoning, perception, and decision-making. This guide is a 2026 reference covering AI's definition, types, core technologies, industry applications, and Turkey-specific regulatory context.

30 min

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blog-bilgisayarli-goru

Vision Transformers or CNNs? A Comparative Analysis of Modern Vision Models

Choosing a model in computer vision is no longer just a question of “which architecture has higher accuracy.” With the rise of Vision Transformers, engineering teams and organizations now need to make more deliberate choices between the long-established practical strengths of CNNs and the scalable representation power of transformer-based visual models. But this decision is often discussed too narrowly through a single benchmark number. In reality, CNNs and Vision Transformers differ substantially in data requirements, inductive bias, training stability, compute profile, inference cost, explainability, edge deployment suitability, and task-specific behavior. This guide compares CNNs and Vision Transformers not only theoretically, but also across classification, detection, segmentation, multimodal systems, and production constraints, showing which approach tends to fit which problem more naturally.

30 min

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blog-dogal-dil-isleme

Where Has Modern NLP Evolved? The Transition from Classical NLP to Transformer-Based Systems

Natural language processing has not merely produced better models over the last decade; it has fundamentally changed how language problems are solved. In the classical NLP era, systems were largely built around rule-based pipelines, feature engineering, statistical language models, and task-specific architectures. Modern NLP, by contrast, has been reshaped by representation learning, large-scale pretraining, transfer learning, self-attention, transformer architectures, and the foundation model paradigm. This transition created major jumps in quality, scale, and flexibility across text classification, information extraction, machine translation, question answering, search, and generative AI. But this is not just a story of “larger models.” It is a redefinition of data usage, context modeling, task abstraction, evaluation, and production AI design. This guide explains the transition from classical NLP to transformer-based systems and shows where modern NLP has evolved, both technically and strategically.

31 min

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blog-uretken-yapay-zeka

How Large Language Models Work: Transformer, Tokenization, Attention, and the Logic of Inference

Large language models have become one of the most influential technologies in modern AI. Yet they are often explained too superficially, as if they were merely “text prediction engines trained on huge amounts of data.” While that description is not entirely wrong, it is far from sufficient. Without understanding transformer architecture, tokenization, self-attention, representation learning, and inference dynamics, it is impossible to understand how LLMs actually behave. This guide provides a systematic and technically grounded explanation of how large language models work, from tokens and embeddings to transformer blocks, attention, training, inference, and sampling.

28 min