CME295

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Transformers and Large Language Models

Course Description

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.

Grading Basis

ROP - Letter or Credit/No Credit

Min

2

Max

2

Course Repeatable for Degree Credit?

No

Course Component

Workshop

Enrollment Optional?

No

Programs

CME295 is a completion requirement for:
  • (from the following course set: )
  • (from the following course set: )