[mark_l_watson]

This will certainly help people who work on new deep learning theories and model architectures, but not so much the large crowd of deep learning practitioners.

In his excellent interview with Lex Fridman, Yann LeCun was critical of any approach to AI that was not differentiable, even constraint satisfaction, and other solid optimization techniques. In the context of scaling to very large problems or models with many billions of parameters, he is probably correct.

I have had problems with the Swift and TensorFlow code drops. Sometimes they work for me and sometime they don’t. So, very good technology but perhaps wait for it to mature. I read that some students for the fast.ai course using Swift have also had some setup difficulties.

EDIT: you might also want to look at Julia for differentiable programming and Julia with deep learning libraries like Flux is also a ‘turtles all the way down’ system, where unlike TensorFlow where the guts are implemented in C++, for Swift and Julia the entire stack can be implemented in a single language.