Regularization

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Regularization is a set of techniques that help a model learn general patterns instead of memorizing the training data. When a model is too flexible, it can fit the training examples perfectly and still fail on new inputs. Regularization adds gentle pressure that keeps learning “reasonable,” so the model stays more stable in real use. A simple example is a model that learns to identify dogs. If it secretly relies on the background of the training photos, it may fail on dogs in a new setting. Regularization helps reduce that kind of brittle shortcut.

Different models use different forms of regularization. Some methods add a penalty during training so the model avoids extreme parameter values. In deep learning, dropout randomly disables parts of the network during training so the model can’t depend on one narrow path. Early stopping ends training when performance on new data stops improving, which helps prevent memorization.

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