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by klintcho
2448 days ago
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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. |
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