Stanford CME295 provides an in-depth journey into Transformers and Large Language Models, the backbone of modern AI and natural language processing. The course begins by introducing the architecture and principles of Transformer models, highlighting how they process sequential data efficiently. Students then explore advanced techniques in Transformer-based models, including optimization tricks, training strategies, and fine-tuning methods for specialized tasks. The curriculum dives into LLM reasoning, showcasing how these models generate coherent, context-aware responses and support complex decision-making. Additionally, learners examine agentic LLMs, which combine reasoning with task-oriented actions, and gain practical insights into evaluating LLM performance across various benchmarks. The course concludes with a comprehensive recap of current trends, emphasizing the latest research directions and real-world applications. Designed for students and professionals alike, CME295 balances theory with hands-on examples, enabling learners to understand both the underlying mechanisms and practical implementations of modern language models. By the end of the course, participants will be equipped with the skills to implement, fine-tune, and assess Transformer-based LLMs for AI projects or research.
