Evolution of the Transformer Architecture Used in LLMs (2017-2025) - Full Course

Explore the evolution of Transformer architecture in large language models through this comprehensive 2-hour 50-minute course designed for beginners using a step-by-step teaching approach. Discover the latest advancements that have enhanced the accuracy, efficiency, and scalability of Transformers from 2017 to 2025. Learn various techniques for encoding positional information, examine different types of attention mechanisms, understand normalization methods and their optimal placement, and study commonly used activation functions. Master the fundamental concepts through detailed explanations of positional encoding, attention mechanisms, and small refinements before putting everything together in practical applications. Access accompanying slides, notebooks, and scripts through the provided GitHub repository to reinforce your learning and apply the concepts hands-on.