Foundations of Large Language Models: Building from Scratch with PyTorch

Large Language Models are the engines driving today's AI revolution, yet their internal workings often seem like a black box. This course demystifies the technology by guiding you through the fundamental building blocks of generative models, focusing on the principles that make them function. You will transition from a curious learner to someone who understands the architecture, training logic, and implementation details of models like GPT-2. By reading through detailed technical explanations and code-based examples, you will gain a clear perspective on how raw data is transformed into intelligent text generation. What you'll learn: - Understand the fundamental concepts of transformer-based architectures and self-attention mechanisms - Implement core model components using PyTorch, focusing on tensor operations and neural network layers - Practice building a GPT-style model architecture from scratch through written code explanations - Apply modern data processing techniques, including tokenization and embedding strategies - Learn about current optimization patterns and basic fine-tuning concepts for generative AI - Explore the transition from raw text data to model-ready input pipelines The course begins with essential terminology and foundational definitions before moving into the step-by-step logic of model construction and training cycles. You will follow a structured path from basic mathematical concepts to the realization of a functional language model architecture. This course is designed for beginners with a basic grasp of Python who want to understand the technical reality of AI models without relying on high-level abstractions. No prior experience with deep learning is required. Start your journey into the architecture of modern language models today.