Understanding LLMs and the Transformer Architecture with PyTorch

Modern AI is dominated by Large Language Models, but the underlying architecture can seem complex. Start your journey by mastering the foundational concepts that enable these powerful generative systems. By the end of this course, you will have a solid theoretical understanding of the Transformer architecture and practical skills to implement core components using PyTorch. You will be able to read and modify basic LLM code, setting the stage for advanced work in NLP and generative AI. What you'll learn: * Learn the history and fundamental concepts of sequence-to-sequence modeling and attention mechanisms. * Understand the complete structure of the Transformer architecture, including encoder, decoder, and positional encoding. * Implement key components of a Transformer model from scratch using the PyTorch library. * Practice tokenization, data preparation, and training loop configuration for language modeling tasks. * Apply basic prompt engineering techniques to interact effectively with generative models. * Explore foundational concepts of Retrieval-Augmented Generation (RAG) to enhance model knowledge retrieval. The course begins with essential terminology and mathematical foundations before moving into the detailed description and implementation of the Transformer components. We progress through practical, text-based coding exercises focused on building a working language model prototype. This course is designed for absolute beginners in AI and machine learning who want to understand the core technology behind Large Language Models. No prior experience with PyTorch or advanced NLP is required. Start building your foundational expertise in generative AI today.