Siraj Raval just posted this video on defending AI against adversarial attacks

Machine Learning technology isn’t perfect, it’s vulnerable to many different types of attacks! In this episode, I’ll explain 2 common types of attacks and 2 common types of defenses using various code demos from across the Web. There’s some really dope mathematics involved with adversarial attacks, and it was a lot of fun reading about the ‘cat and mouse’ game between new attack techniques, followed by new defense techniques. I encourage anyone new to the field who finds this stuff interesting to learn more about it. I definitely plan to. Let’s look into some math, code, and examples. Enjoy!

Slideshow for this video:
https://colab.research.google.com/drive/19N9VWTukXTPUj9eukeie55XIu3HKR5TT

Demo project:
https://github.com/jaxball/advis.js

 

In this hour long tutorial, learn the basics of the NumPy library in this tutorial for beginners.

It provides background information on how NumPy works and how it compares to Python’s Built-in lists.

It starts with the basics of creating arrays and then gets into more advanced stuff. The video covers creating arrays, indexing, math, statistics, reshaping, and more.

Code –> https://github.com/KeithGalli/NumPy

It slices! It dices! And it’s hard to imagine doing data science in Python without it. NumPy is a keystone Python library that’s crucial to learn. Fortunately,  Giles McMullen has you covered with this five minute tutorial of NumPy!