[keghn]

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data: 

https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463

[selimchehimi]

That's very interesting, I didn't know that there were that many kinds of neural networks! Where do you think I can start to learn the basic one?

[keghn]

Autoencoder is central to the NN world. Learn about artificial neuron, then a feed forward NN, then a generative NN. And then
the autoencoder.

A wizard’s guide to Adversarial Autoencoders: Part 1, Autoencoder?: 
https://medium.com/towards-data-science/a-wizards-guide-to-adversarial-autoencoders-part-1-autoencoder-d9a5f8795af4

[LOCKSUIT]

" “If you know how to write a code to classify MNIST digits using Tensorflow, then you are all set to read the rest of this post or else I’d highly suggest you go through this article on Tensorflow’s website.” "

I don't know at all how to do that. Yet I can fluently understand EVERYTHING he talks about, and what he doesn't know!!

[keghn]

 The autoencoder has two parts. The front end, which is the detector and it could be tensorflow. Then the autoencoder has a second part that regenerates what is detected. Could regenerated the detection in a weird dream like state: 

https://www.google.com/search?q=deepdreams&tbm=isch&imgil=1SQGC5w8UVAfQM%253A%253BHdR1vsFpX9j32M%253Bhttp%25253A%25252F%25252Fwww.ibtimes.co.uk%25252Fgoogle-deepdream-robot-10-weirdest-images-produced-by-ai-inceptionism-users-online-1509518&source=iu&pf=m&fir=1SQGC5w8UVAfQM%253A%252CHdR1vsFpX9j32M%252C_&usg=__MchY87ZymQEv2VIROnhVN1_bMWA%3D#imgrc=1SQGC5w8UVAfQM 

 Or recreate with super vision resolution: 
 

 "General  Adversarial  Networks" are like autoencoders in that they have a detector and a generator to recreate what they 
see. 

[selimchehimi]

Autoencoder is central to the NN world. Learn about artificial neuron, then a feed forward NN, then a generative NN. And then
the autoencoder.

A wizard’s guide to Adversarial Autoencoders: Part 1, Autoencoder?: 
https://medium.com/towards-data-science/a-wizards-guide-to-adversarial-autoencoders-part-1-autoencoder-d9a5f8795af4

Thank you, I will check that now!