weight decay
Weight decay is a regularization technique used in machine learning to prevent overfitting by adding a penalty term to the loss function that encourages the model to learn simpler patterns. It works by adding a term to the loss function that penalizes large weights, thus discouraging the model from fitting noise in the data. By doing so, weight decay helps improve the generalization performance of the model.
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