some tutorials

I often like to write down in my own words topics in ML that I have spent significant time learning about. Instead of keeping it on paper, I thought I might as well write it out on the internet, forcing me to distill and clarify my own understanding.

resources

This guide takes inspiration and information from many sources. Listed here are some great additional resources for learning

• transformers: https://web.stanford.edu/~jurafsky/slp3/

• diffusion and flow monograph: https://www.arxiv.org/abs/2510.21890

• flow models: https://d2jud02ci9yv69.cloudfront.net/2025-04-28-flow-with-what-you-know-38/blog/flow-with-what-you-know/

Planned Table of Contents

• Reinforcement Learning

  • Offline RL

  • RL for LLMs

  • Successor Representations

• Diffusion/Flow

  • Score Matching

  • Diffusion Models as Score Matching with Langevin Dynamics

  • Diffusion Models from denoising probablistic models

  • Flow Matching and Diffusion from ODEs

• Misc

  • anything in ML

thanks to Kevin Frans for the nice website @ https://kvfrans.com/