Available in العربية - English - Español - فارسی - Français - 한국어 - Português - Türkçe - Tiếng Việt - 简中 - 繁中
This repository aims at summing up in the same place all the important notions that are covered in Stanford's CS 229 Machine Learning course, and include:
- Refreshers in related topics that highlight the key points of the prerequisites of the course.
- Cheatsheets for each machine learning field, as well as another dedicated to tips and tricks to have in mind when training a model.
- All elements of the above combined in an ultimate compilation of concepts, to have with you at all times!
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| Supervised Learning | Unsupervised Learning | Deep Learning | Tips and tricks |
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| Probabilities and Statistics | Algebra and Calculus |
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| All the above gathered in one place |
This material is also available on a dedicated website, so that you can enjoy reading it from any device.
Would you like to see these cheatsheets in your native language? You can help us translating them on this dedicated repo!
Afshine Amidi (Ecole Centrale Paris, MIT) and Shervine Amidi (Ecole Centrale Paris, Stanford University)