In the autograd tutorial 20, under the section “Gradients”, we call out.backward() to use automatic differentiation to compute d(out)/dx. After calling .backwards(), the field x.grad is available.
But why do we want to do this? I expected that we would want to compute d(out)/d(parameters). Does this call to .backwards() also update the network parameters, and if not, what is the use case?