
ml-retreat
Machine Learning Journal for Intermediate to Advanced Topics.
Stars: 2169

ML-Retreat is a comprehensive machine learning library designed to simplify and streamline the process of building and deploying machine learning models. It provides a wide range of tools and utilities for data preprocessing, model training, evaluation, and deployment. With ML-Retreat, users can easily experiment with different algorithms, hyperparameters, and feature engineering techniques to optimize their models. The library is built with a focus on scalability, performance, and ease of use, making it suitable for both beginners and experienced machine learning practitioners.
README:
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.
![]() Build an LLM from Scratch |
![]() LLM Hallucination |
![]() LLM Edge: Beyond Attention |
![]() Introduction to GNNs |
![]() AlphaFold3: a machine learning look |
![]() Natural Language Processing |
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 |
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
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