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Machine Learning cheatsheets for Stanford's CS 229

A collection of concise, downloadable machine-learning cheatsheets and refreshers for Stanford's CS229 course. It includes PDFs covering supervised, unsupervised and deep learning, tips & tricks, and prerequisite refreshers (probability, algebra, calculus). Available in multiple languages and compiled as an all-in-one super cheatsheet.

Introduction

Overview

The "Machine Learning cheatsheets for Stanford's CS 229" repository is a compact, well-organized compilation of high-value reference materials for students and practitioners of machine learning. Its main goal is to gather the essential concepts taught in Stanford's CS229 course and present them as short, easy-to-scan PDFs and cheat sheets so users can quickly review algorithms, formulas, practical tips, and prerequisite math.

Main Content
  • VIP Cheatsheets: Ready-to-download PDFs focused on core ML areas — Supervised Learning, Unsupervised Learning, Deep Learning — plus a dedicated "Tips and Tricks" sheet containing practical advice for model training and evaluation.
  • Refreshers: Short refreshers on foundational prerequisites such as probabilities & statistics and algebra & calculus to help users recall the mathematical tools used throughout the course.
  • Super Cheatsheet: An "all-in-one" compilation that aggregates the most important formulas and concepts from the individual sheets into a single, printable reference.
Features
  • PDF-first approach: The materials are provided as printable PDF cheat sheets for quick reference during study, coding sessions, or interviews.
  • Multilingual: The repo contains translations (Arabic, Spanish, Persian, French, Korean, Portuguese, Turkish, Vietnamese, Simplified and Traditional Chinese, etc.), making the content accessible to non-English speakers.
  • Lightweight & Practical: Emphasis is on concise formulas, algorithm summaries, and actionable tips rather than long theoretical exposition — ideal for revision and applied work.
Who it's for
  • Students taking Stanford's CS229 or equivalent machine learning courses.
  • Practitioners needing a quick reminder of algorithm equations, loss functions, or training heuristics.
  • Instructors and study groups who want compact handouts.
Authors & provenance

The cheatsheets were prepared by Afshine Amidi and Shervine Amidi and are also mirrored on a dedicated course website. The GitHub repository provides easy access, multi-language folders, and direct links to the PDF resources.

How to use
  • Browse the language folder that fits you and download the relevant PDF (e.g., supervised, unsupervised, deep learning, tips).
  • Use the super cheatsheet when you need a compact single-page summary.
  • Contribute translations or corrections via the translation repository referenced in the README.
Notes
  • The repository is intended as a study/reference supplement and not a full textbook — it prioritizes brevity and practicality.
  • Files are freely available for personal and educational use; check the repo for licensing details.

Information

  • Websitegithub.com
  • AuthorsAfshine Amidi, Shervine Amidi
  • Published date2018/08/04