Here we’re going to provide 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
- Classification
- Clustering
- Neural networks
- … and much more
Links to individual cheat sheets related to Machine Learning are below:
- Supervised learning
- Unsupervised learning
- Deep learning
- Tips and tricks
- Probability and stats refresher
- Algebra and calculus refresher
- Machine Learning Super Cheat Sheet
Links to individual Cheat Sheets related to Deep Learning are below
Links to individual Cheat sheets related to artificial intelligence are below.
You can visit Shervine’s CS 229 resource page or the Github repo for more information, or can download the cheat sheet from the direct download links above.
You can also find all of the sheet bundled together into a single “super VIP cheat sheet.”
Thanks to Shervine and Afshine for putting these fantastic resources together.
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