Machine Learning Cheat Sheets
Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus.
Cheat sheets for machine learning are plentiful. Quality, concise technical cheat sheets, on the other hand... not so much. A good set of resources covering theoretical machine learning concepts would be invaluable.
Shervine Amidi, graduate student at Stanford, and Afshine Amidi, of MIT and Uber, have created just such a set of resources. The VIP cheat sheets, as Shervine and Afshine have dubbed them (Github repo with PDFs available here), are structured around covering key top-level topics in Stanford's CS 229 Machine Learning course, including:
- Notation and general concepts
- Linear models
- Neural networks
- ... and much more
Links to individual cheat sheets are below:
- Supervised learning
- Unsupervised learning
- Deep learning
- Tips and tricks
- Probability and stats refresher
- Algebra and calculus refresher
You can also find all of the sheets bundled together into a single "super VIP cheat sheet."
Thanks to Shervine and Afshine for putting these fantastic resources together.
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