13 posts
What is a neural network? A neural network is a machine learning model inspired by the neurons in the human brain, learning patterns from data through connected layers of artificial neurons. This guide: a clear definition, neuron and layer structure, activation functions, learning via backpropagation, types of neural networks, real-world examples, its relation to deep learning, and FAQs.
What is machine learning? Machine learning is the branch of AI that lets a computer produce predictions or decisions by learning patterns from data, rather than being explicitly programmed with rules. This guide: a clear definition, how machine learning works, supervised and unsupervised learning, model training, examples, use cases, limits and FAQs.
What is a GPU? A GPU (Graphics Processing Unit) is hardware designed for parallel processing that runs the same operation across massive data simultaneously with thousands of small cores. This guide: a clear definition, how a GPU works, the difference from a CPU, VRAM, CUDA, its role as AI hardware, examples, limits, and FAQs.
What is computer vision? Computer vision is the field of AI that lets a machine perceive and understand the content of images and videos like a human. This guide: a clear definition, how it differs from image processing, how computer vision works, CNNs and deep learning, object detection and image classification, enterprise use cases, and FAQs.
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
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.
Detailed 12-month roadmap to become an AI engineer from zero: Month 1-2 Python + math foundation, Month 3-4 classic ML, Month 5-6 deep learning + PyTorch, Month 7-8 LLM + RAG + agentic, Month 9-10 MLOps + production, Month 11-12 specialized + job search. Each month with specific courses (Coursera, fast.ai, DeepLearning.AI), books, milestone projects, Turkish resources (BTK Akademi, Coursera Turkish subtitles), daily study plan, portfolio requirements (5-10 GitHub projects), Kaggle strategy, certifications, job application tactics. SMB/freelance/abroad options.
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.
One of the most misunderstood topics in deep learning is the assumption that training success and real performance are the same thing. In reality, low training error, strong validation metrics, or short-term impressive outputs do not always mean that a model generalizes well, behaves reliably, or remains robust in the real world. Overfitting happens when a model adapts too strongly to dataset-specific noise and patterns instead of learning the underlying structure. Underfitting happens when the model fails to capture even the core structure of the problem. Generalization is the model’s ability to perform consistently on unseen data. This guide explains overfitting, underfitting, and generalization not only conceptually, but through the lenses of data, model capacity, regularization, evaluation, training dynamics, and production AI.
Model architecture is often the most visible design decision in deep learning, but some of the most decisive factors for training success are optimizer, learning rate, and loss function selection. The same model architecture can learn at a very different speed, converge more or less stably, generalize differently, or fail entirely depending on how these three components are configured. The optimizer determines how the model moves through parameter space, the learning rate controls the size of that movement, and the loss function defines what the model is actually optimizing. These three components are therefore not independent choices, but tightly coupled parts of the same training dynamics. This guide explains the theory, practice, task-based selection logic, common failure modes, and production implications of choosing optimizers, learning rates, and loss functions in deep learning.
Three of the most commonly confused concepts in deep learning are transfer learning, fine-tuning, and representation learning. They are not the same thing, but they are tightly connected. Representation learning refers to learning useful and generalizable internal features from data. Transfer learning is the broader strategy of reusing knowledge learned in one task or domain for another task or domain. Fine-tuning is often the practical adaptation mechanism used to realize that transfer. Put differently, strong representations make transfer possible, transfer learning defines the reuse logic, and fine-tuning operationalizes it. This guide explains the historical development, conceptual relationship, practical differences, and enterprise relevance of these three ideas in modern AI systems.
One of the most common mistakes in deep learning projects is assuming that a model with strong training metrics is ready for production. In reality, high accuracy, low loss, or strong validation performance do not guarantee readiness under real user traffic, distribution shift, latency constraints, security requirements, observability needs, failure handling, version control, or operational sustainability. Production success depends not only on model architecture, but also on data pipelines, inference design, model packaging, serving infrastructure, monitoring, rollback strategy, evaluation discipline, governance, and workflow integration. This guide explains why moving from training to production in deep learning projects requires much more than a good model, and what a production-grade AI system actually needs.
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.