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Teaching  
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CS 229 ― Machine Learning
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My twin brother Afshine and I created this set of illustrated Machine Learning cheatsheets covering the content of the CS 229 class, which I TA-ed in Fall 2018 at Stanford. They can (hopefully!) be useful to all future students of this course as well as to anyone else interested in Machine Learning.

Cheatsheet

Illustration Supervised Learning
Results about linear models, generative learning, support vector machines and kernel methods
September 2018
Illustration Unsupervised Learning
Formulas about clustering methods and dimensionality reduction
September 2018
Illustration Deep Learning
Main concepts around neural networks, backpropagation and reinforcement learning
September 2018
Illustration Tips and tricks
Good habits and sanity checks to make sure that your model is trained the right way
September 2018

Refresher

Illustration Probabilities and Statistics
Formulas about combinatorics, random variables, main probability distributions, and parameter estimation
August 2018
Illustration Linear Algebra and Calculus
Matrix-vector notations as well as algebra and calculus properties
August 2018