Princeton: Understanding Large Language Models

Overview

This is a Princeton University graduate course (COS 597G) that provides a technical deep dive into large language models. It covers the foundations, architecture, training, and capabilities of LLMs through lecture notes and readings.

Best for

Best for
Researchers, students, and developers seeking a rigorous conceptual foundation in large language models

Use cases

• Gaining a thorough theoretical understanding of transformer-based language models
• Studying the training objectives, scaling laws, and emergent abilities of LLMs
• Accessing curated lecture materials for self-study or curriculum design

Notes

This is a Princeton University graduate course (COS 597G) that provides a technical deep dive into large language models. It covers the foundations, architecture, training, and capabilities of LLMs through lecture notes and readings.

Use cases

• Gaining a thorough theoretical understanding of transformer-based language models
• Studying the training objectives, scaling laws, and emergent abilities of LLMs
• Accessing curated lecture materials for self-study or curriculum design

Pros

• Authoritative academic content from a leading computer science department
• Covers both foundational concepts and recent research developments
• Freely available lecture notes and reading list

Cons

• Not a hands-on coding framework or build tool
• Designed as a course, so structure may feel rigid for non-students
• Content from fall 2022 may not include the very latest developments

Indexed from awesome-llm and enriched against its public facts.