Resources

Overview:

• • Dan Shiffman being awesome and making Dan Shiffman videos on Neural Networks and General Machine Learning (and the math needed)

    • They are in Javascript and sometimes use p5.js but are fairly general/understandable/useful even if you do not know Javascript

Linear Algebra:

• • 3Blue1Brown Essence of Linear Algebra

    • Gorgeous animated videos about the intuition behind linear algebra

  • Khan Academy Linear Algebra

    • arguably the best content on the web teaching linear algebra concepts/theory

    • concise yet complete

  • fast.ai Computational Linear Algebra for Coders

    • exercise/programming based curriculum

Python/Programming:

• Official Python Tutorial

  • Learn X in Y minutes (Python cheatsheet)

• Think Python

  • UC Berkeley CS 61A (intro to computer science)

Machine Learning:

• • 3Blue1Brown Neural Networks

  • fast.ai (great online courses in deep learning)

    • hands-on project based curriculum

    • geared towards coders

  • Andrew Ng's general ML Courses: Coursera and Stanford

• Resources to Learn ML/AI

  • mathematicalmonk YouTube channel (more theoretical/math heavy)

  • AI and Deep Learning in 2017 review (and the wildml.com site in general)

  • distill.pub (interactive explanations of ML concepts and research)

  • Arthur Juliani's blog (ML tutorials mainly centered around reinforcement learning)

ML + Art:

• Deep Learning in an Artistic Context

• A Return to Machine Learning

  • Google's Artists and Machine Intelligence blog

  • The Magenta project

Glossaries:

• Google Machine Learning (and TensorFlow) Glossary

• Metacademy

  • UC Berkeley Glossary of Statistical Terms

• Linear Algebra Glossary