This comprehensive Stanford Online series covers Large Language Models (LLMs) and Transformer architectures, providing learners with an in-depth understanding of the technology driving modern AI. Starting with foundational concepts in Transformers, tokenization, and architecture design, the lectures explore advanced topics such as LLM training, tuning, reasoning, evaluation, and agentic models.
Students will gain practical insights into the latest research trends, techniques for optimizing LLM performance, and methods to assess model outputs effectively. Key lectures also discuss the biology of LLMs, real-world applications, and challenges in scaling and deploying large models.
Designed for students, researchers, and AI enthusiasts, this course provides both theoretical foundations and hands-on knowledge to work with state-of-the-art language models. By following this series, learners will acquire the skills to understand, build, and evaluate Transformers and LLMs, and stay updated with the current trends in AI research.
