Good points. One comment: I use GANs at work. While useful they can be difficult to train. Sometimes the loss function for the combined discriminator and generator does not decrease with training as much as you would like, but the generator used on its own is still useful. Goodfellow, inventor of GAN, only spends about one page in his long deep learning book on GANs. RNNs also make good generators and are easier to train. Anyway, GANs may not be good for practical how-to tutorials.

Edit: good advice on using Keras. Keras is ‘understandable’ in the sense that reading the code for Keras itself is useful and Francois Chollet, creater of Keras, has a fantastic new book out - which I strongly recommend.