If you have any existing ML/DL experience, picking up PyTorch is a breeze. You could get a pretty solid understanding of the framework with an MNIST handwritten digit recognition model over the course of an afternoon, so don’t sweat looking for the “right” tutorial.

Awesome, I've done some ML before. I'll just dive into the docs.

Highly recommend it! I love pytorch so much, it's basically numpy with automatic backprop and CUDA support. It evaluates eagerly by default, which makes debugging a lot easier since you can just print your tensors, and IMO it's much simpler to jump between high-level and low-level details in pytorch than in tensorflow+keras. Just as one example, activation functions in pytorch are applied by calling a python function on your layer, instead of passing a string argument with the function name to the layer constructor, so you write

  layer = F.relu(Linear()(input))

instead of

  layer = Dense(activation_fn='relu')(input)

As a result, it's a lot more straightforward to try out custom activation functions in PyTorch.

Pytorch website has a “blitz” tutorial that was fantastic.