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Guide · Open source

How to use cheatsheets-ai: browse and download ML cheat sheets

Updated · written with AI from the project's own documentation and checked against our catalogue data

cheatsheets-ai is an open-source collection of one-page visual summaries of commands and concepts for deep learning, machine learning and data tools. This guide is for students, practitioners and instructors who want a quick reference while coding or studying. By the end you will know how to browse the sheets online, download them, or clone the repository.

AI Cheatsheetskailashahirwar/cheatsheets-ai · ★ 15.4k · MIT

Quick start

  1. 1Open the website. Go to aicheatsheets.com to browse the sheets in a web view.
  2. 2Pick a tool. Choose the sheet for the library you are working with, such as Pandas or TensorFlow.
  3. 3Download or clone. Run git clone https://github.com/kailashahirwar/cheatsheets-ai to get a local copy of all the files.
  4. 4Open the PDF or image. Open the sheet as a PDF or image, or print it for your desk.
  5. 5Check the official docs. Confirm arguments and current behaviour in the library's documentation before using them.

What is cheatsheets-ai and what does it cover?

cheatsheets-ai is a set of quick-reference cheat sheets. Each sheet is a one-page visual summary of the main commands and concepts for a tool. The project describes itself as a handy reference while you work or study, not as a full course or a software library.

  • Deep learning and ML frameworks: TensorFlow, Keras, Neural Networks Zoo, Neural Network Cells and Graphs
  • Data tools: NumPy, SciPy, Pandas (three sheets), Dask (four sheets)
  • Plotting: Matplotlib, Seaborn, ggplot2
  • Big data: PySpark, PySpark-RDD, PySpark-SQL
  • R: Data Wrangling with dplyr and tidyr

How do you browse the cheat sheets online?

The sheets are available as images and PDFs. The project's website is aicheatsheets.com. You can also open the files directly from the repository's file list on GitHub, where each sheet has its own link.

Start with the sheet for the tool you are using. Each one is a single page, so you can scan it quickly for the syntax you need.

  • Open aicheatsheets.com for the website version.
  • Use the repository listing to open a single sheet as an image or PDF.
  • Look for 'All Cheat Sheets (PDF)' if you want every sheet in one file.

How do you download the cheat sheets?

If you want a local copy, clone the repository. The README gives this command:

  • Save the files to a folder you can open offline or print.
  • Print the one-page sheets for a desk reference or a classroom handout.
git clone https://github.com/kailashahirwar/cheatsheets-ai

How do you use a cheat sheet while coding?

Open the sheet next to your editor or notebook. Find the function group you need, such as Pandas data selection or NumPy array creation, and copy the pattern into your code. Check the official documentation for the exact arguments before you rely on a call in production.

The sheets are most useful as a reminder of syntax you have already seen, rather than as a first introduction to a tool.

  • Pick the sheet that matches your library and version of the task.
  • Use it to recall function names and common patterns.
  • Confirm details in the library's own documentation.

What should you keep in mind before relying on it?

The sheets assume some familiarity with each tool. Complete beginners who need step-by-step teaching should start with a course or the official documentation. The repository's last push was in October 2019, and newer library versions may differ from what the sheets show.

The project is licensed under the MIT License, so you can read the license file in the repository for the terms.

  • Not a course: the sheets do not teach from first principles.
  • Not runnable code: the files are static images and PDFs.
  • Check current API details against the official docs for the library version you use.

What are good alternatives and companions?

The project lists these comparable resources. Use cheatsheets-ai for a quick visual reminder, and use these for depth and up-to-date detail.

  • Official TensorFlow and PyTorch documentation
  • Python Data Science Handbook
  • Awesome Machine Learning

What people use it for

  • A student scanning a one-page summary of NumPy or Pandas functions before an exam.
  • A practitioner checking the syntax of a PySpark SQL operation while writing a notebook.
  • An instructor printing the Keras or Scikit-learn sheet as a classroom handout.
  • Someone browsing which ML and data tools exist, using the list of covered libraries as an overview.

Frequently asked questions

Is cheatsheets-ai free to use?

Yes. The repository is licensed under the MIT License, and the license file is included in the repo. You can read the terms there before reusing the files.

Do I need to install anything to use cheatsheets-ai?

No installation is required. The sheets are images and PDFs you can read online or download. The README lists no prerequisites, and the only command given is git clone to get a local copy.

How do I download all the cheat sheets?

Run git clone https://github.com/kailashahirwar/cheatsheets-ai to copy the repository locally. There is also an 'All Cheat Sheets (PDF)' file in the repo that combines the sheets.

Is cheatsheets-ai good for complete beginners?

Not really. The sheets assume some familiarity with each tool. Beginners who need step-by-step teaching should use a course or the official documentation first, then use the sheets as a reminder.

Are the cheat sheets up to date?

Not necessarily. The repository's last push was in October 2019, and newer library versions may differ from what the sheets show. Check the official documentation for current API details.

What are alternatives to cheatsheets-ai?

The project lists the official TensorFlow and PyTorch documentation, the Python Data Science Handbook, and Awesome Machine Learning as comparable resources. These offer more depth and detail than a one-page sheet.

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Our verdict

cheatsheets-ai is a good free reference for students and practitioners who already know a library and want a quick visual reminder of its syntax. Beginners who need teaching, or anyone who needs current API details or runnable code, should pick a course, the official documentation or another resource instead.

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