GAN - Generative Adversarial Networks and creative generative art

Hi Dan,

Here is a link to a video about "Progressive Growing of GANs for Improved Quality, Stability, and Variation". I think it captures the amazing things you can do with neural networks.
[Demonstration of GANs](https://www.youtube.com/watch?v=XOxxPcy5Gr4)

Also, there are many videos online on "deep dreams" [An example](https://www.youtube.com/watch?v=SCE-QeDfXtA)

All of these videos were created with GANs\Generative Adversarial Networks. These are a class of neural networks, mainly in unsupervised learning. [Wikipedia Link](https://en.wikipedia.org/wiki/Generative_adversarial_network)

They were used for the [Pix2Pix model](https://affinelayer.com/pixsrv/) also see [Pix2Pix Article](https://arxiv.org/abs/1611.07004).

Sentdex has recently done a series about unusual neural networks, where he covered GANs. There is also a Computerphile video on this subject. These videos are amazing about learning how GANs work, but they don't cover their inner workings. Also, I haven't seen anyone on YouTube talk about the cool art that can be made with GANs, and the creative coding opportunities.

Maybe it would be interesting to have a mini-series about GANs, something like "Generative Adversarial Networks for Artists". Starting with how GANs really work and then moving on to TensorFlow.js and [the hvass-labs tutorials](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/14_DeepDream.ipynb) and finally have community contributions and showcase community art.

I know you'll be able to make this interesting and easy to understand.

Hope you like my idea. Best regards,

Eitan

Beginne mit der Durchsicht der verlinkten Ressourcen zu Progressive Growing of GANs, Pix2Pix, TensorFlow.js und hvass-labs DeepDream. Definiere eine klar abgegrenzte Mini-Serie zu den Grundlagen von GANs, Creative Coding, TensorFlow.js, dem referenzierten Tutorial und künstlerischen Beiträgen aus der Community; abgeschlossen ist die Aufgabe, wenn der Umfang der Serie und der Beitragsweg vereinbart sind.

Vom Indexierungsmodell aus dem Issue-Text verfasst.