Weight decay is a regularization technique used in machine learning to prevent overfitting by adding a penalty term to the loss function that discourages overly complex models. It effectively reduces the magnitude of the weights in the model, encouraging simpler models that generalize better to unseen data. By applying weight decay, it helps to control the complexity of the model and improve its performance on validation datasets.
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