What made me fall in love with PyTorch was also that the "neural network training process" is defined almost as it is in theory, in code in PyTorch

- loop through epochs

- loop through each batch

- run a forward pass for the batch ( model(batch) )

- calculate the loss for the batch ( criteria(y, yprim)

- compute the gradients/backprop ( loss.backward() )

- update the weights (optimizer.step())

This really enforced everything I learned and I think breaks down the problem. All this of course in addition to everything else already mentioned, and super convenient module/network building and definition.