CME 295 - Transformers & Large Language Models

This course explores the world of Transformers and Large Language Models (LLMs). You'll learn the evolution of NLP methods, the core components of the Transformer architecture, along with how they relate to LLMs as well as techniques to enhance model performance for real-world applications. Through a mix of theory and practical insights, this course will equip you with the knowledge to leverage LLMs effectively. Ideal for those with a background in calculus, linear algebra, and basic machine learning concepts.

Syllabus

Cheatsheet

Canvas

Course staff

Afshine Amidi
Instructor

Shervine Amidi
Instructor

General information

Class communication primarily happens on Canvas > Ed Discussion.
Course content is listed in the syllabus and will be updated as the quarter goes.
A public-facing cheatsheet summarizes in a concise and illustrated way the main concepts covered in the class.
For general inquiries, please contact cme295-aut2526-staff@lists.stanford.edu.

Course characteristics

In-person lectures on Fridays 3:30pm - 5:20pm in Thornton 110.
Class is recorded.
No homework. However, there are two exams: a midterm and a final.

Class textbook

Super Study Guide: Transformers & Large Language Models

Available at the Stanford library or online.

CME 295 - Transformers & Large Language Models

Instructors

Logistics

Course staff

General information

Course characteristics

Class textbook