Transformer Architecture: Code and Math Fundamentals

Modern generative AI relies heavily on the Transformer architecture, but its inner workings often seem like a black box. This course demystifies the core concepts. By focusing purely on code and essential mathematics, you will build a complete, functional generative model from the ground up, gaining a deep, practical understanding of how these powerful systems are constructed and trained. What you'll learn: * Understand the purpose and structure of the masked multi-head attention mechanism and positional encoding. * Implement the full pipeline, from input tokenization and vocabulary creation to the final text generation process. * Build the complete multi-layer Transformer block, integrating feed-forward networks and normalization layers. * Apply fundamental optimization techniques and loss functions required to train large neural networks effectively. * Practice debugging and iterating on your model components to ensure stable and coherent text generation results. The course begins with foundational concepts and mathematical prerequisites before moving into the practical implementation of each architectural component. You will progressively code the entire model structure and conclude with the training and generation phases. This course is designed for beginners in deep learning or programming who want to understand the foundational mechanics of generative models. No prior experience with complex neural network architectures is required, only basic programming familiarity. Start your journey toward mastering the architecture that powers modern AI.