PyTorch has become the de facto standard for research (meaning cutting edge models often have their sole implementation in PyTorch), and TF has had a much more unstable and uh, baroque API. It should be mentioned that while some of that API mess probably could have been avoided, other parts of it are a consequence of TF's view of computational graphs as static rather than dynamic, which gives it a fundamental performance advantage over PyTorch. (You can think of this as vaguely equivalent to why compiled languages can run faster than interpreted languages.)

I take it you've never had to depend upon a Google library before.

Just kidding, but yeah - my understanding is that Tensorflow V1 was annoying to use and that Tensorflow V2 sort of "pytorch-ified" everything but it was a.) breaking changes and b.) too little too late.

Now Google has JAX (they JIT numpy so it will run well on their TPU's + autograd) which is coming along nicely and takes a different approach from pytorch. I'll still be using pytorch or a wrapper for pytorch-isms however.

From the book's Amazon page:

"PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric."

This is a pretty good write-up: https://nicodjimenez.github.io/2017/10/08/tensorflow.html