Translations:Overfitting and Regularization/15/en: Difference between revisions

Latest revision as of 23:34, 27 April 2026

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Message definition (Overfitting and Regularization)

{{Term|weight decay|L2 regularization}} is equivalent to placing a Gaussian prior on the weights from a Bayesian perspective. It encourages small, distributed weights and discourages any single weight from becoming excessively large.

L2 regularization is equivalent to placing a Gaussian prior on the weights from a Bayesian perspective. It encourages small, distributed weights and discourages any single weight from becoming excessively large.

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	L2 regularization is equivalent to placing a Gaussian prior on the weights from a Bayesian perspective. It encourages small, distributed weights and discourages any single weight from becoming excessively large.		{{Term\|weight decay\|L2 regularization}} is equivalent to placing a Gaussian prior on the weights from a Bayesian perspective. It encourages small, distributed weights and discourages any single weight from becoming excessively large.