PyTorch Training Loop
PyTorch tensors carry a computation graph; the canonical loop is forward pass, loss computation, loss.backward() to populate gradients via autograd, optimizer.step(), and zero_grad(). Models subclass nn.Module to register parameters, and tensors plus module must be moved to the same device (.to('cuda')) — device placement, not math, is the usual first bug.
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PyTorch tensors carry a computation graph; the canonical loop is forward pass, loss computation, loss.backward() to populate gradients via autograd, optimizer.step(), and zero_grad(). Models subclass nn.Module to register parameters, and tensors plus module must be moved to the same device (.to('cuda')) — device placement, not math, is the usual first bug.
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