A loss function is what guides a model during training, translating predictions into a signal it can improve on. But not all losses behave the same—some amplify large errors, others stay stable in noisy settings, and each choice subtly shapes how learning unfolds. Modern libraries add another layer with reduction modes and scaling effects that […]
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