Summary
This post explains the internal mechanisms of deep learning libraries, detailing how models learn by converting loss into parameter updates. It breaks down the process, covering how a forward pass builds a computation graph, how `loss.backward()` applies the chain rule to compute gradients, and how optimizers use these gradients to update model parameters.
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