# kailashahirwar/cheatsheets-ai

Essential Cheat Sheets for deep learning and machine learning researchers https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5

Repository: https://github.com/kailashahirwar/cheatsheets-ai
Canonical: https://ross.abutalabs.com/products/cheatsheets-ai
Homepage: https://aicheatsheets.com
License: MIT
License Family: permissive
Topics: deep-learning, artificial-intelligence, machine-learning, keras, matplotlib, scipy, numpy
Last push: 2019-10-19T12:33:06+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": 3388, "days_push": 2510, "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 15427, forks 3372 (observed 2026-08-28T04:11:09.807926+00:00)

## What it is
A curated collection of cheat sheets (PDFs and images) covering machine learning and deep learning tools like TensorFlow, Keras, PyTorch, NumPy, Pandas, Scikit-learn, Matplotlib, and PySpark. It serves as a quick-reference resource for ML/DL researchers and engineers rather than runnable software.

## Use cases
- quickly look up keras or tensorflow api syntax
- find a numpy or pandas cheat sheet for data manipulation
- reference sheet for scikit-learn functions while studying machine learning
- cheat sheet for matplotlib and seaborn plotting
- learn neural network architectures with a visual zoo chart
- pyspark and r data wrangling quick reference

## When to choose
- you want quick visual reference cards for popular ML/DL libraries
- you are studying for interviews or learning ML frameworks
- you need offline PDF/image references for common APIs

## When to avoid
- you need up-to-date documentation for current library versions (content dates to ~2019)
- you want interactive tutorials or runnable code examples
- you need comprehensive documentation rather than condensed summaries

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, machine-learning, deep-learning, data-science
- domain: machine-learning, deep-learning, data-science, tutorials
- platform: cross-platform
- tags: cheatsheets, reference, tensorflow, keras, numpy, pandas, scikit-learn, matplotlib, pyspark, r

## Member repositories
- kailashahirwar/cheatsheets-ai (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:09.807926+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 2026-08-29T17:05:54.369981+00:00, confidence not recorded.
  - readme: https://github.com/kailashahirwar/cheatsheets-ai (fetched 2026-08-28T04:11:09.807926+00:00, sha 8d252fe1d112)
- Data as of 2026-08-30T08:39:29.467469+00:00.