Guide · Open source
How to use stanford-cs-229-machine-learning: cheatsheets guide
Updated · written with AI from the project's own documentation and checked against our catalogue data
stanford-cs-229-machine-learning is an open-source collection of short PDF cheatsheets that summarise the main topics of Stanford's CS 229 Machine Learning course. It is aimed at students taking or reviewing the course, and at engineers who want a compact refresher. By the end of this guide you will know what the repository contains, how to get the files, and which cheatsheet to open for a given topic.
CS 229 Machine Learning cheatsheetsafshinea/stanford-cs-229-machine-learning · ★ 20.4k · MIT Install options & downloads Quick start
- 1Decide what you need. Identify whether you need a topic cheatsheet, a refresher, or the combined super cheatsheet.
- 2Clone the repository. Run the git clone command from the README to download the files to your computer.
- 3Open the English folder. Find the PDFs in the en folder of the cloned repository.
- 4Open the relevant PDF. Pick the supervised, unsupervised, deep learning or tips and tricks sheet for the topic you are studying.
- 5Review refreshers when needed. If a topic depends on probability, statistics, algebra or calculus, open the matching refresher.
- 6Check against the course. Compare the summary with your lecture notes or textbook for derivations and detail.
What does the stanford-cs-229-machine-learning repository contain?
The repository collects cheatsheets for Stanford's CS 229 Machine Learning course in one place. According to its README, the goal is to sum up the important notions covered in the course, including refreshers on the prerequisites and cheatsheets for each machine learning field.
- VIP Cheatsheets: Supervised Learning, Unsupervised Learning, Deep Learning, and Tips and tricks
- VIP Refreshers: Probabilities and Statistics, and Algebra and Calculus
- Super VIP Cheatsheet: all of the above gathered in one document
How do you get the files onto your computer?
The README gives one install step: clone the repository with git. The repo holds PDFs and README content only, so there is nothing to build or configure afterwards. You can also open the PDFs directly from the GitHub links in the README, or use the website the authors provide.
- Clone the repository, then open the PDF you need from the en folder
- Or open the PDFs on GitHub without cloning
- Or read the material on the dedicated website from any device
git clone https://github.com/afshinea/stanford-cs-229-machine-learningWhich cheatsheet should you open first?
Choose by the topic you are studying. The supervised and unsupervised learning sheets cover the two main families of methods. The deep learning sheet covers neural networks, and the tips and tricks sheet covers practical points to keep in mind when training a model. If a course concept depends on maths you have not used recently, open one of the refreshers first.
- Supervised learning: cheatsheet-supervised-learning.pdf
- Unsupervised learning: cheatsheet-unsupervised-learning.pdf
- Deep learning: cheatsheet-deep-learning.pdf
- Training tips: cheatsheet-machine-learning-tips-and-tricks.pdf
- Probability and statistics: refresher-probabilities-statistics.pdf
- Algebra and calculus: refresher-algebra-calculus.pdf
- Everything combined: super-cheatsheet-machine-learning.pdf
How can you use the cheatsheets alongside the course?
Because the cheatsheets are summaries, use them as a review tool rather than a substitute for lectures or notes. A common approach is to read the relevant cheatsheet after a lecture to check your recall, and to use the refreshers when a topic assumes probability or linear algebra you need to brush up on.
The README also notes that the material is available in many languages, including Arabic, Spanish, French, Portuguese, Chinese and Vietnamese, and that translation help is welcome.
- Read the matching cheatsheet after each lecture topic
- Use the refreshers when a topic relies on probability or algebra
- Check the super cheatsheet when you want everything in one file
What is this repository not good for?
The repository contains only PDFs and README content. It does not include runnable code, software to install, or full lecture material. The cheatsheets do not contain in-depth derivations. The README's own material also has a last push date of May 2020, so check whether a topic has changed since then before relying on it.
- No runnable code or software to install
- No in-depth derivations or full lectures
- Content may not reflect updates after May 2020
What people use it for
- Reviewing core supervised learning concepts before an exam
- Brushing up on probability and statistics before starting a machine learning course
- Keeping a compact reference on a phone or tablet
- Checking practical tips for training a model
- Refreshing linear algebra and calculus basics for an engineering role
Frequently asked questions
Is stanford-cs-229-machine-learning free?
The repository is licensed under MIT, according to its listing. The cheatsheets are also available on the authors' website, so you can read them without cloning the repo.
Do I need to install anything to use it?
No. The install method is read-online, and no prerequisites are listed. You only need git if you want a local copy of the PDFs.
Does it include code or runnable examples?
No. The repository contains only PDFs and README content, so it is not a source of runnable code or software.
Is it the full Stanford CS 229 course?
No. It is a set of cheatsheets that summarise the main topics. For full lecture material and in-depth derivations, you need course notes or lectures.
Is the content up to date?
The README lists a last push date of May 2020. Check any topic that may have changed since then against current sources.
What can I use instead or alongside it?
The project lists Stanford CS 229 course notes, Deep Learning book companion resources and Hands-on ML with Scikit-Learn resources as comparable projects. Those are useful if you need more depth or code.
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Our verdict
Use this repository if you are taking or reviewing CS 229 or a similar course and want short, readable summaries of core machine learning and maths topics. Pick something else if you need runnable code, detailed derivations, or content that is current after May 2020.
Install options & downloads