I would like to know why you are comparing these two environments? I think your info is great with some nice links to good material.
Are you evaluating the strengths and weaknesses of both platforms? Or do you just need to work with both for your work and it just is nice to document the differences. Just curious :)
Thanks for your comment!
I volunteered and committed to give a talk about using Deep learning in R through Keras for the Orlando Data Science and Machine Learning Meetup. The people in that group mostly use Python, but R/Keras was considered interesting enough.
While working on the presentation it became apparent that it is much easier to visualize and illustrate the "deep learning as a LEGO set" perspective using Mathematica / WL. (Also, the partitioning of the operations/layers is made in a slightly different way.)
Another thing is that with Mathematica I can much easier illustrate and visualize some analogies to "shallow" non-neural network algorithms.
Since the presentation was going to be relatively short compared to the material I wanted to cover, I decided to start a GitHub project that provides a systematic exposition of the considered functionalities and applications. At that point making a Mathematica-vs-R project made lots of sense. Obviously, comparing the two systems is a good way to learn about neural networks.
After my presentation fair amount of the attendees were interested in Mathematica. I did tell to the group that if they a serious about figuring out and mastering Deep learning they should get hold of Mathematica.
(As for my work I have experimented applying Deep learning with H2O through R for quite some time... And yes I at this point I also use Keras and MXNet.)