// the find
afshinea/stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning
A set of PDF cheatsheets covering the syllabus of Stanford's CS229 machine learning course — supervised learning, unsupervised learning, deep learning, and general tips/tricks, plus math refreshers on probability and linear algebra. Good for someone who took an ML course once and needs a fast reference for formulas and definitions, not for someone learning the material from scratch.
The content is dense and well organized by topic instead of being one giant dump — you can grab just the deep learning sheet without wading through everything else. The 'super cheatsheet' that merges all of them into one document is a nice touch for exam-style review. Translated into a dozen languages by community contributors, which is unusual reach for a personal project. The math refreshers (algebra/calculus, probability/stats) are a genuinely useful inclusion most ML cheatsheet repos skip.
It's PDFs, not text or markdown — you can't grep it, diff it, or easily pull a formula into your own notes without retyping it. No code, no worked examples, no exercises, so it's only useful if you already understand the concepts and need a memory jog, not for learning them. Last updated in 2020, so there's nothing on transformers, LLMs, or anything past classic supervised/unsupervised ML — it's frozen at a 2017-era course syllabus. No sources or derivations for the formulas, so if something looks off you have no way to check it against the original math.