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 toplevel topics in Stanford's CS 229 Machine Learning course, including:
 Notation and general concepts
 Linear models
 Classification
 Clustering
 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 visit Shervine's CS 229 resource page or the Github repo for more information, or can download the cheat sheets from the direct download links above.
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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