What it does

This repository is a living bibliography of machine learning and deep learning links, organized into broad categories: cheatsheets, framework-specific tutorials (Keras, TensorFlow, PyTorch, JAX), courses, papers, and tools. It spans everything from Stanford CS 229 VIP cheatsheets to 7-hour TensorFlow walkthroughs on YouTube. The maintainer, Mikhail Dozmorov, also points to a companion repo for genomics and programming notes.

The interesting bit

The curation has a researcher’s bias—there’s a dedicated “DL Genomics” subsection and a sudden, recent addition of “Claude Code for Babies” and Gemini CLI tips, suggesting the maintainer is actively using (and annotating) these tools rather than just collecting them. The “Neural Network Zoo” infographic link is a nice touch for the visually oriented.

Key highlights

• Dense coverage of entry points: cheatsheets range from 5-page algorithm quick references to 100-slide “Machine Learning 101” decks
• Framework tutorials include both Python and R ecosystems (notable Keras/TensorFlow RStudio integration)
• Sections on graph neural networks, transformers, and auto-ML reflect reasonably current (if not bleeding) trends
• Explicitly welcomes PRs and has a CONTRIBUTING.md

Caveats

• No quality filtering is visible—dead links, outdated TensorFlow 1.x material, and paywalled Manning books sit alongside free resources without distinction
• The “Awesome Deep Learning” and “Awesome Machine Learning” sections are just nested lists of other awesome-lists, which may frustrate those seeking original curation
• README is truncated in the source; full depth of the “ML Papers” and tool subsections is unclear

Verdict

Worth bookmarking if you’re a graduate student or self-learner who wants one starting point for surveying the ML landscape. Skip it if you need evaluated, annotated recommendations or a learning path rather than a firehose of links.