Translations:Overfitting and Regularization/22/en: Difference between revisions

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    '''{{Term|dropout}}''' (Srivastava et al., 2014) is a regularization technique specific to neural networks. During training, each neuron is randomly "dropped" (set to zero) with probability <math>p</math> at each forward pass. This prevents neurons from co-adapting and forces the network to learn redundant representations.
    '''Dropout''' (Srivastava et al., 2014) is a regularization technique specific to neural networks. During training, each neuron is randomly "dropped" (set to zero) with probability <math>p</math> at each forward pass. This prevents neurons from co-adapting and forces the network to learn redundant representations.

    Revision as of 22:02, 27 April 2026

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    Message definition (Overfitting and Regularization)
    '''{{Term|dropout}}''' (Srivastava et al., 2014) is a regularization technique specific to neural networks. During training, each neuron is randomly "dropped" (set to zero) with probability <math>p</math> at each forward pass. This prevents neurons from co-adapting and forces the network to learn redundant representations.

    Dropout (Srivastava et al., 2014) is a regularization technique specific to neural networks. During training, each neuron is randomly "dropped" (set to zero) with probability $ p $ at each forward pass. This prevents neurons from co-adapting and forces the network to learn redundant representations.

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