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: )