Machine-Learning-Cheat-Sheet by Frank Dai.
From the Preface:
This cheat sheet contains many classical equations and diagrams on machine learning, which will help you quickly recall knowledge and ideas in machine learning.
This cheat sheet has three significant advantages:
1. Strong typed. Compared to programming languages, mathematical formulas are weakly typed. For example, X can be a set, a random variable, or a matrix. This causes difficulty in understanding the meaning of formulas. In this cheat sheet, I try my best to standardize symbols used, see section §.
2. More parentheses. In machine learning, authors are prone to omit parentheses, brackets and braces, this usually causes ambiguity in mathematical formulas. In this cheat sheet, I use parentheses(brackets and braces) at where they are needed, to make formulas easy to understand.
3. Less thinking jumps. In many books, authors are prone to omit some steps that are trivial in his option. But it often makes readers get lost in the middle way of derivation.
Two other advantages of this “cheat-sheet” are that it resides on Github and is written using the Springer LaTeX template.
Neural networks can be easily fooled, Deep Neural Networks are Easily Fooled:… so the question becomes, how easy is it to fool the machine learning algorithms summarized by Frank Dai?
Or to put it another way, if I know the machine algorithm most likely to be used, what steps, if any, can I take to shape data to influence the likely outcome?
Excluding outright false data because that would be too easily detected and possibly trip too many alarms.
The more you know about how an algorithm can be cheated, the safer you will be in evaluating the machine learning results of others.
I first saw this in a tweet by Kirk Borne.