as i struggle to get my act together, this has been immensely helpful in piecing things together
http://cs231n.github.io/convolutional-networks/ https://www.toptal.com/machine-learning/generative-adversarial-networks
in particular, all the different names for same items, OR similar names for different items were killing me.
so, if you ever have questions, here were a couple of helpful answers i found:
https://stats.stackexchange.com/questions/193793/in-convolutional-neural-networks-cnn-how-we-can-decide-number-of-kernels-betw/193953#193953
https://www.quora.com/What-does-the-number-of-filters-in-a-convolution-layer-convey-How-does-this-number-effect-the-performance-or-quality-of-the-architecture
basic tutorials really helped too:
https://github.com/uclaacmai/Generative-Adversarial-Network-Tutorial/blob/master/Generative%20Adversarial%20Networks%20Tutorial.ipynb
https://www.tensorflow.org/get_started/mnist/pros
http://cv-tricks.com/tensorflow-tutorial/training-convolutional-neural-network-for-image-classification/
https://pythonprogramming.net/tensorflow-neural-network-session-machine-learning-tutorial/?completed=/tensorflow-deep-neural-network-machine-learning-tutorial/
python programming was AMAZING. just saying.
https://github.com/pytorch/pytorch?utm_source=mybridge&utm_medium=blog&utm_campaign=read_more
if anyone ever wants to use pytorch instead of tensorflow (personally i would love to try pytorch!!)
other stuff on GANs: https://hackernoon.com/how-do-gans-intuitively-work-2dda07f247a1 https://towardsdatascience.com/implementing-a-generative-adversarial-network-gan-dcgan-to-draw-human-faces-8291616904a
interesting take on GANs
https://openreview.net/pdf?id=ryj38zWRb