Weight decay is a regularization technique used in machine learning to prevent overfitting by adding a penalty term to the loss function. This penalty discourages overly complex models by shrinking the weights of the network during training, ultimately leading to a smoother, more generalized model. It is particularly important in deep learning scenarios where models, such as recurrent neural networks, may easily memorize training data instead of learning to generalize from it.
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