This Stanford CME295 course is an advanced bootcamp focused on transformers and large language models (LLMs), providing a deep understanding of the technologies behind modern generative AI systems. The course explores both the theoretical foundations and practical techniques used in state-of-the-art AI research and development.

You will begin by learning the transformer architecture, the core technology powering modern AI systems such as ChatGPT and other language models. The course explains attention mechanisms, sequence modeling, and how transformers process language efficiently at scale.

Next, you will explore transformer-based models and optimization techniques commonly used to improve performance, scalability, and training efficiency. These lessons provide insight into how modern AI architectures are designed and refined.

The bootcamp also focuses on large language models, explaining how LLMs are trained on massive datasets and how they develop advanced language understanding and generation capabilities. You will study important topics such as pretraining, tokenization, and model scaling.

Advanced lectures cover LLM training workflows and fine-tuning techniques used to adapt models for specialized tasks and real-world applications.

By the end of this course, you will have a strong understanding of transformer systems, LLM architectures, training strategies, and tuning methods used in modern generative AI development.

This course is ideal for AI researchers, machine learning engineers, and advanced students interested in NLP and large-scale AI systems.