The Parallax View: Lecture 5: Machine Learning 2.

What is Machine Learning? What is Deep Learning?
Let's watch Introduction to Deep Learning by Teubi at the FANTASTIC (I watch live every year) Chaos Communication Congress - the 2018 wiki is a great resource.
Also see Daniel Shiffman's "A Beginner's Guide to Machine Learning with ml5.js" - particularly his discussions on algorithms, models and data sets - particularly what we are doing - using existing models, rather than training our own - and criticality - we should be using machine learning for art and design so we can critcise goverments (and others) use of it.

It's important to remember that there are limitations to Deep Learning.
Interesting article on the difference between supervised and unsupervised learning.
Computerphile has some great videos on neural networks, deep learning and other interesting things.
Also see the "Machine Learning for the Web" class at ITP, NYU for some great notes, exercises and examples.
Lets look at some ml5.js examples from the ml5.js website.

This example, Video Classification, is based on Daniel Shiffman's tutorial "ml5.js: Webcam Image Classification" and Kyle McDonalds CV Examples.
Remember - the model we are using is only trained on a few image classes. It can't see anything it hasn't been trained for! This is something to bare in mind whenever you use someone elses model - what has it been trained for? What biases might it have? Via Daniel Shiffman's tutorial "ml5.js: Image Classification with MobileNet".
Also try this YOLO (You Only Look Once) (Chrome Only!) demo again from Kyle McDonalds CV Examples.

Unfortunately, there is currently a bug with getting ml5.js working with the p5.js editor.
So we'll have to run our code locally, and run a local server so that we can see it.
Download this zip file containing a live video style transfer example to your desktop. It will only work if you run your own local web server and view the demo in Chrome.
If you want to learn how to train your own models (advanced!), you can follow these two tutorials from The Coding Train: "Style Transfer Part 1: Training a model with on Spell with Yining Shi" and "Style Transfer Part 2: Real-Time Style Transfer with ml5.js with Yining Shi".

Let's try this example of live tracking of body pose. Based on Kyle McDonalds CV Examples.
What could you use this for? This was only previously possible using a desktop computer and a 3D sensor, such as the Microsoft Kinect. A newer version of the Kinect runs the just announced Hololens 2.
An example of the cutting edge of this kind of work is OpenPose.

In your pairs, what do you want your computer to learn?
About what is inside images?
About the style of images?
About the movement of humans?
See this great playlist by Daniel Shiffman: "A Beginner's Guide to Machine Learning with ml5.js".
See this list of AI Experiments from Google.
Also see the Machine Learning 4 Artists (ML4A) website by Gene Kogan.
All works should be published on the brand new p5.js Web Editor if possible, but local copies are ok - make sure you email me the complete zip if you just work locally!

In session 3 we drew on Kyle McDonald's cv-examples. Today, we'll be connecting with him live.
What questions do you have for Kyle?
Let's connect with him now.

Thanks!