Teaching

Machine Learning

  1. 🗺️ Policy Optimization and RL Algorithms

  2. 📈 A Note about KL Divergence

  3. 🗂️ A Taxonomy of Reinforcement Learning Algorithms

  4. 🔎 Monte Carlo Tree Search

  5. 🎛️ How do Mixture of Expert Models Work?

ML Systems

  1. 🪈 ML at Scale: Pipeline Parallelism

  2. 🪆 ML at Scale: Tensor Parallelism

  3. 💽 ML at Scale: Data Parallelism

Notes

  1. 📝 Introduction to RL

  2. 📦 Archive: The Daily Ink Paper Breakdowns

  3. 🎲 Probability and Random Processes Cheat Sheet