Transformers and Large Language Models (LLMs)
A Stanford University course exploring the evolution of NLP methods, the core components of the Transformer architecture, how they relate to LLMs, and techniques to enhance model performance for real-world applications.
This course explores the world of Transformers and Large Language Models (LLMs). It covers the evolution of NLP methods, the core components of the Transformer architecture, how they relate to LLMs, and techniques to enhance model performance for real-world applications.
Through a mix of theory and practical insights, the course equips students with the knowledge to leverage LLMs effectively.
Course website: cme295.stanford.edu/syllabus
Taught by Afshine Amidi and Shervine Amidi, Adjunct Lecturers at Stanford University.
This is a curated third-party resource. All credit and citation belongs to Afshine Amidi, Shervine Amidi, and Stanford University.