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

Commencez par examiner les ressources liées sur Progressive Growing of GANs, Pix2Pix, TensorFlow.js et hvass-labs DeepDream. Définissez une mini-série au périmètre clairement délimité couvrant les fondamentaux des GAN, le creative coding, TensorFlow.js, le tutoriel référencé et les contributions artistiques de la communauté ; le travail est terminé lorsque le périmètre de la série et le parcours de contribution sont convenus.

Rédigé par le modèle d'indexation à partir du texte de l'issue.