This episode of Fresh Machine Learning is all about a relatively new concept called a Generative Adversarial Network. A model continuously tries to fool another model, until it can do so with ease. At that point, it can generate novel, authentic looking data! Very exciting stuff.

The demo code for this video is a set of adversarial Gaussian Distribution Curves in Python using Theano and PyPlot:

https://github.com/llSourcell/Generative-Adversarial-Network-Demo

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I introduce two papers in this video

Generative Adversarial Networks:

https://arxiv.org/pdf/1406.2661v1.pdf

and the associated code:

https://github.com/goodfeli/adversarial

Generative Adversarial Text-to-Image Synthesis:

https://arxiv.org/pdf/1605.05396v2.pdf

and it's associated code is here:

https://github.com/reedscot/icml2016

Another really cool repo using GANs:

https://github.com/Newmu/dcgan_code

Great explanation of GANs:

http://soumith.ch/eyescream/

Live demo of a GAN:

http://cs.stanford.edu/people/karpathy/gan/

One more really great description of generative models:

https://openai.com/blog/generative-models/

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