# bfortuner/ml-glossary

Machine learning glossary

Repository: https://github.com/bfortuner/ml-glossary
Canonical: https://ross.abutalabs.com/products/ml-glossary
Homepage: http://ml-cheatsheet.readthedocs.io
Language: Python
License: MIT
License Family: permissive
Topics: machine-learning, cheatsheets, neural-network, deep-learning, deep-learning-tutorial, data-science
Last push: 2024-08-08T03:16:06+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3422, "days_push": 755, "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 3131, forks 731 (observed 2026-08-28T04:07:45.526109+00:00)

## What it is
An open-source machine learning glossary published as a Sphinx-hosted documentation site, covering neural networks and deep learning concepts with explanations, visuals, equations, and Python code snippets. It serves as a concise reference/cheatsheet for ML learners.

## Use cases
- learn machine learning fundamentals with visual explanations
- quickly look up neural network concepts and equations
- find concise deep learning cheatsheets
- study ML concepts with python/numpy code examples
- reference material while taking an ML course

## When to choose
- you want concise, visual explanations of ML and deep learning concepts
- you need a quick reference with equations and code snippets
- you are a beginner looking for accessible ML tutorials

## When to avoid
- you need up-to-date coverage of modern LLM techniques
- you need a comprehensive textbook-style treatment
- you need actively maintained content - the project seeks new maintainers

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, machine-learning
- domain: machine-learning, deep-learning, data-science, tutorials
- platform: python
- tags: glossary, cheatsheet, neural-networks, sphinx, educational, web-server

## Member repositories
- bfortuner/ml-glossary (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:45.526109+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 2026-08-30T07:26:09.095086+00:00, confidence not recorded.
  - readme: https://github.com/bfortuner/ml-glossary (fetched 2026-08-28T04:07:45.526109+00:00, sha e26f1ef5d1dc)
- Data as of 2026-08-30T08:39:29.467469+00:00.