# soulmachine/machine-learning-cheat-sheet

Classical equations and diagrams in machine learning

Repository: https://github.com/soulmachine/machine-learning-cheat-sheet
Canonical: https://ross.abutalabs.com/products/machine-learning-cheat-sheet
Homepage: http://soulmachine.me
Language: TeX
License Family: other
Last push: 2024-07-30T22:02:41+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": 4864, "days_push": 764, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8046, forks 1332 (observed 2026-08-28T04:10:12.163797+00:00)

## What it is
A LaTeX-compiled cheat sheet PDF containing classical equations and diagrams of machine learning concepts. It serves as a quick reference for recalling ML knowledge and preparing for job interviews.

## Use cases
- quickly recall machine learning equations and concepts
- prepare for machine learning job interviews
- review classical ML algorithms and diagrams
- find a concise ML reference document
- study machine learning theory formulas

## When to choose
- you need a compact reference of classical ML equations and diagrams
- you are preparing for ML-related interviews
- you want a free downloadable PDF summary of ML theory

## When to avoid
- you need hands-on code examples or tutorials with implementations
- you want up-to-date coverage of deep learning and modern techniques
- you need an interactive learning platform rather than a static document

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, machine-learning
- domain: machine-learning, tutorials, education
- platform: cross-platform
- tags: cheat-sheet, latex, reference, interview-preparation, pdf

## Member repositories
- soulmachine/machine-learning-cheat-sheet (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:12.163797+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 2026-08-29T17:31:25.150457+00:00, confidence not recorded.
  - readme: https://github.com/soulmachine/machine-learning-cheat-sheet (fetched 2026-08-28T04:10:12.163797+00:00, sha 1b2354586a17)
  - homepage: http://soulmachine.me (fetched 2026-08-29T08:29:05.135753+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.