AI Cheatsheets
A collection of downloadable cheat sheet images and PDFs for machine learning and data tools.kailashahirwar/cheatsheets-ai
- Stars
- 15.4k
- Forks
- 3.4k
- Licence
- MIT
- Language
- —
- Last update
- 7.1 years ago
- Type
- Learning resource
No commits in over a year — check for forks or alternatives before adopting.
What it is
This repository is a set of quick-reference cheat sheets covering common deep learning, machine learning, and data tools such as TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, PySpark, and Dask. Each sheet is a one-page visual summary of commands and concepts, provided as images and PDFs. It is meant as a handy reference while you work or study, not as a full course or software library.
- Open the Pandas or NumPy sheet while writing a data-cleaning script
- Print the Scikit-learn or Matplotlib sheet to keep at your desk
- Browse the Neural Network Zoo sheet to compare architecture types
- Download the All Cheat Sheets PDF for offline reference
Great for
- Students who want a one-page summary of a library's main functions
- Practitioners who need a quick reminder of syntax while coding
- People browsing an overview of which ML and data tools exist
- Instructors looking for printable reference material
Not ideal for
- Complete beginners who need step-by-step teaching, since the sheets assume some familiarity with each tool
- Anyone needing up-to-date API details, because the last push was in October 2019 and newer library versions may differ
- People looking for runnable code or a software package, since this is a set of static reference files
How to get it
- 1
Read it online
Clone the repository
git clone https://github.com/kailashahirwar/cheatsheets-ai - 2
Run it
- Open https://aicheatsheets.com or the repository on GitHub
- Pick a cheat sheet from the list, such as Numpy or Pandas-1
- Open the image or PDF in your browser or download it
- Print it or keep it open alongside your code
- 3
Get the most out of it
- Use the All Cheat Sheets PDF to keep a single offline copy
- Check the Medium article linked in the README for the author's background on the sheets
- Pair each sheet with the official documentation for the library to confirm current syntax
Downloads
Strengths
- Covers many widely used tools in one place
- Free to use under the MIT License
- Sheets are available as both images and PDFs for easy viewing or printing
Trade-offs
- Last updated in October 2019, so some content may be out of date
- The README does not describe how the sheets are kept current or who maintains them
- Reference material only; it does not teach concepts in depth
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| cheatsheets-ai (this) | 15.4k | Learning resource | beginner | 7.1 years ago | Students who want a one-page summary of a library's main functions |
| PythonDataScienceHandbook | 50.3k | Learning resource | beginner | 2.4 years ago | Students learning NumPy, Pandas, Matplotlib, or Scikit-Learn through hands-on examples |
| AI-For-Beginners | 70.5k | Learning resource | beginner | 13 days ago | Beginners who want a structured, self-paced introduction to AI |
| AI-Expert-Roadmap | 31.3k | Learning resource | beginner | 1.2 years ago | Beginners who are unsure what to learn next in AI or data science |
| machine-learning-for-trading | 21.7k | Learning resource | intermediate | 8 days ago | Readers of the Machine Learning for Trading book who want to run its code |
| mit-deep-learning | 10.5k | Learning resource | beginner | 2.8 years ago | Students following MIT deep learning courses |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.