Reading

This is a collection of things I have read that led to enjoyment or learning, and thus wanted to note down for future reference. Some are things that I have not read yet, but think will be enjoyable or valuable.

Things on the internet:

• jsomers.net | I should have loved biology
• Main Content | Jeremy Kun
• GPT in 60 Lines of NumPy
• An Intuition for Attention
• The Annotated Transformer
• Neural network training makes beautiful fractals | Jascha’s blog
• colah’s blog
• projects | Bones
• Sorta Insightful | Alex Irpan
  • I’m Switching Into AI Safety
  • “How Do You Feel About Grad School?” and The 5 Year Update on Skipping Grad School (and Whether I’d Recommend It)
• Deep Generative Models | CS 326 Notes
• Lil’Log | Lilian Weng
• An Opinionated Guide to ML Research
• Neel Nanda
  • Become a person who Actually Does Things
  • An Extremely Opinionated Annotated List of My Favourite Mechanistic Interpretability Papers v2
  • Concrete Steps to Get Started in Transformer Mechanistic Interpretability
  • Learning how to learn
• On Those Undefeatable Arguments for AI Doom
• ~agentydragon/How I got to OpenAI

Books:

• Linear Algebra Done Right
• The Algorithm Design Manual
• Understanding Deep Learning
• Deep Learning - Foundations and Concepts
• An Infinite Descent into Pure Mathematics
• A Programmer’s Introduction to Mathematics
• Poor Charlie’s Almanack
• The Napkin
• Nonlinear Dynamics and Chaos
• Alice’s Adventures in a Differentiable Wonderland — Volume I, A Tour of the Land

Papers:

• Relational NN
• Minimum Description Length Principle
• [Autodidax: JAX core from scratch](https://jax.readthedocs.io/en/latest/autodidax.html
• Automatic Gradient Descent: Deep Learning without Hyperparameters

Other:

• Vagabond

Compilations:

• rsrch space | varepsilon
• Greg Yang Book List