ML Retreat: Advanced ML Learning Journal

Current Grind: Mechanistic Interpretability

This repository is my personal journal of learning advanced topics in machine learning. It includes an in-depth understanding of fundamentals + additional must-read/watch recourses for more nuanced subjects.

📝 My Notes

     ![](assets/LLM-from-scratch-notes.png) 
      

    [Build an LLM from Scratch](assets/LLM-from-scratch-notes.pdf)
  

  

     ![](assets/LLM-hallucination.jpg) 
      

    [LLM Hallucination](assets/LLM-Hallucination.pdf)
  

  

     ![](assets/LLM-Edge-Beyond-Attention.png) 
      

    [LLM Edge: Beyond Attention](assets/LLM-Edge-Beyond-Attention.pdf)
  





  

     ![](assets/introduction-to-GNN.png) 
      

    [Introduction to GNNs](assets/introduction-to-GNN.pdf)
  

  

     ![](assets/AlphaFold3.png) 
      

    [AlphaFold3: a machine learning look](assets/AlphaFold3.pdf)
  

  

     ![](assets\Natural-Language-Processing.png) 
      

    [Natural Language Processing](assets\Natural-Language-Processing.pdf)

📕 Table of Content

If you go to th Days Folder you can find a list of all the topics I have covered. However, for easier access to a specific subject, check out this table to find which days to go through.

Subject

Check out:

Large Language Models

from Day 003 to Day 016

Graph Neural Networks

from Day 017 to Day 022

AlphaFold 3

Day 23

🎯 Goals

My goals of this learning retreat includes studying:

• Ilya Sutskever's top 30 must-read research papers
• Most of Distilled AI's Blogs
• Artem Kirsanov's AI/ML Playlist
• Andrej Karpathy's golden Neural Net Playlist
• In-depth understanding/implementations of Transformers
• LLMs and related topics ✅
• LLM Halucination in depth ✅
• Quantum Machine Learning
• Jax
• Energy-Based Models