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Dropout is a regularization technique for reducing overfitting in neural networks by preventing complex co-adaptations on training data. It is a very efficient way of performing model averaging with neural networks.[1] The term "dropout" refers to dropping out units (both hidden and visible) in a neural network.[2]

See alsoEdit


  1. ^ Hinton, Geoffrey E.; Srivastava, Nitish; Krizhevsky, Alex; Sutskever, Ilya; Salakhutdinov, Ruslan R. (2012). "Improving neural networks by preventing co-adaptation of feature detectors". arXiv:1207.0580 [cs.NE].
  2. ^ "Dropout: A Simple Way to Prevent Neural Networks from Overfitting". Retrieved July 26, 2015.


  1. ^ Warley-Farde et al,1312.6197 An empirical analysis of dropout in piecewise linear networks,2014(