Neural-Networks

Table of Contents Introduction Part 1: Mathematical Foundations Part 2: Neural Network Fundamentals Part 3: Understanding Transformers Part 4: Large Language Models Explained Part 5: Training and Fine-Tuning LLMs Part 6: Practical Implementation Resources and Learning Paths Conclusion Introduction The rise of Large Language Models (LLMs) has revolutionized artificial intelligence and natural language processing. From ChatGPT to Claude to Gemini, these powerful systems can understand context, generate human-like text, and solve complex problems across domains. But how do they work? And more importantly, how can you learn to build them from scratch? ...

Modern large language models (LLMs) like GPT-4, Llama, and Claude look magical—but they are built on concepts that have matured over decades: neural networks, gradient descent, and clever architectural choices. This tutorial walks you step by step from classic neural networks all the way to LLMs. You’ll see how each idea builds on the previous one, and you’ll get practical code examples along the way. Table of Contents Foundations: What Is a Neural Network? 1.1 The Perceptron 1.2 From Perceptron to Multi-Layer Networks 1.3 Activation Functions ...

Large language models (LLMs) are transformative AI systems trained on massive text datasets to understand, generate, and predict human-like language. They power tools like chatbots, translators, and code generators by leveraging transformer architectures, self-supervised learning, and intricate mechanisms like attention.[1][2][4] This comprehensive guide breaks down LLMs from fundamentals to advanced operations, drawing on established research and explanations. Whether you’re a developer, researcher, or curious learner, you’ll gain a detailed understanding of their inner workings. ...