Compilation of ML/Stats Resources

One frustrating thing about the modern day Internet is that high-quality content (educational) resources are often burried by SEO-optimized content. This is a compilation of resources that I found useful.

Courses

• Machine Learning from Human Preferences course note. Lecture recordings are also available.
• Learning Deep Representations of Data Distributions UC Berkeley textbook
• Foundations of Computer Vision

LLMs

Inference, scaling, etc

• BentoML LLM Inference Handbook
• How to Scale Your Model
• The Ultra-Scale Playbook: Training LLMs on GPU Clusters
• Reinforcement Learning from Human Feedback Book

Statistics

• https://statproofbook.github.io/
• NYU Probability and Statistics for Data Science

Blogs

• Karthick’s Blog