LLMs in Depth: How an LLM Works Mathematically (and Its Implementation with PyTorch)

Demystify the mathematics behind Large Language Models and implement them from scratch in PyTorch. This advanced workshop takes you through the complete mathematical foundations: attention mechanisms, transformer architecture, positional encodings, layer normalization, and training dynamics. For each mathematical concept, we'll write the corresponding PyTorch implementation—giving you a deep, hands-on understanding of how LLMs actually work under the hood.