# afshinea/stanford-cs-229-machine-learning

VIP cheatsheets for Stanford's CS 229 Machine Learning

Repository: https://github.com/afshinea/stanford-cs-229-machine-learning
Canonical: https://ross.abutalabs.com/products/stanford-cs-229-machine-learning
Homepage: https://stanford.edu/~shervine/teaching/cs-229
License: MIT
License Family: permissive
Topics: cheatsheet, machine-learning, data-science, supervised-learning, unsupervised-learning, deep-learning, ml-cheatsheet, cs229
Last push: 2020-05-20T04:57:25+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2951, "days_push": 2296, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 20161, forks 4254 (observed 2026-08-28T04:11:29.789877+00:00)

## What it is
A collection of illustrated PDF cheatsheets and refreshers summarizing Stanford's CS 229 Machine Learning course, covering supervised learning, unsupervised learning, deep learning, and training tips. Available in many languages, it condenses key concepts, math prerequisites, and model-training tricks into quick-reference documents.

## Use cases
- review machine learning concepts before an exam
- find a quick reference for supervised and unsupervised learning algorithms
- brush up on linear algebra and probability prerequisites for ML
- study deep learning fundamentals like backpropagation and CNNs
- get a summary of model evaluation metrics and cross-validation tips
- learn Stanford CS 229 course material in my native language

## When to choose
- you want concise, well-illustrated summaries of core ML theory
- you are taking or teaching a course based on Stanford CS 229
- you need quick-reference PDFs covering ML math prerequisites
- you prefer study material in one of the many available translations

## When to avoid
- you need hands-on code examples or runnable notebooks
- you want comprehensive tutorials rather than condensed summaries
- you need up-to-date coverage of recent ML techniques like transformers or LLMs
- you are looking for an interactive learning platform

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, machine-learning, deep-learning
- domain: machine-learning, deep-learning, data-science, tutorials, education
- platform: cross-platform
- tags: cheatsheets, cs229, study-notes, pdf, supervised-learning, unsupervised-learning, multilingual

## Member repositories
- afshinea/stanford-cs-229-machine-learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:29.789877+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:59:19.769917+00:00, confidence not recorded.
  - readme: https://github.com/afshinea/stanford-cs-229-machine-learning (fetched 2026-08-28T04:11:29.789877+00:00, sha e11d8fb1bafe)
  - homepage: https://stanford.edu/~shervine/teaching/cs-229 (fetched 2026-08-29T07:58:13.636570+00:00, sha dab21978714d)
- Data as of 2026-08-30T08:39:29.467469+00:00.