What You Need to Know Before
You Start
Starts 5 June 2025 10:37
Ends 5 June 2025
00
days
00
hours
00
minutes
00
seconds
54 minutes
Optional upgrade avallable
Not Specified
Progress at your own speed
Free Video
Optional upgrade avallable
Overview
Delve into the internal mechanisms of Claude 3.5 Haiku through Anthropic's circuit tracing methodology, exploring how large language models function at a fundamental level.
Syllabus
- Introduction to Large Language Models
- Understanding Neural Network Architectures
- Foundations of Circuit Tracing Methodology
- Claude 3.5 Haiku's Model Internals
- Functional Analysis of Claude 3.5 Haiku
- Practical Circuit Tracing Techniques
- Implications of Circuit Tracing for AI Development
- Summary and Recap
Overview of Large Language Models (LLMs)
Historical context and evolution of LLMs
Introduction to Claude 3.5 Haiku
Overview of neural network fundamentals
Transformer architecture
Claude 3.5 Haiku's specific architecture
Introduction to circuit tracing
Importance in understanding LLM functionality
Methodological steps involved
Examination of key components
Attention mechanisms and their role
Internal layer functionalities
Tracing key circuits in Claude 3.5 Haiku
Understanding context and token processing
How Claude generates coherent outputs
Tools and software for circuit tracing
Analyzing decision-making paths in LLMs
Case studies and practical exercises
Insights gained from detailed circuit analysis
Limitations and challenges of current methods
Future directions and applications in AI research
Key takeaways from the course
Open questions in LLM research
Preparing for Part 2: Advances and Applications of LLMs
Subjects
Computer Science
