- RESOURCES
Github repository for this class
Web Development
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P5 - website for one of the primary Javascript libraries we will be using for this class
Machine Learning
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ml5 - this is the machine learning library we will be using, along with p5js
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The Coding Train - Dan Shiffman’s legendary and highly entertaining YouTube channel. This has everything from intro to p5 tutorials to neural network deep-dives.
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distill.pub - Interactive demos of many machine learning principles
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3Blue1Brown - 3Blue1Brown neural network series
Advanced resources:
Given the wide range of abilities in this class, it will be difficult to move at a pace everyone agrees with. Given this, I openly welcome students to try and work outside of the materials presented in this class and research/implement emerging technologies independently. Below are some more advanced resources for this.
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ml4a is run by Gene Kogan and has a series of excellent and comprehensive guides, demos, classes, and code written primarily in python.
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Machine Learning for Artists and Musicians Rebecca Fiebrink is the creator of Wekinator, a machine learning platform meant for real time performance
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Fast.ai is an excellent free MOOC started by Rachel Thomas and Jeremy Howard. They have several course - machine learning, deep learning, cutting edge deep learning, and linear algebra.
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Andrew Ng’s popular Coursera course for those who want to learn more about the nuts and bolts of machine learning
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Stanford CS231N - Convolutional Neural Networks for Visual Recognition at Stanford
Additional resources:
Links to interesting topics related to the class, but not necessary for coursework
- Runway - A new toolkit that aims to remove the need to code in creating machine learning creative work.