« Extinction de neurone » : différence entre les versions


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== Domaine ==
== Domaine ==


[[catégorie:Démo]] Catégorie Démo
[[category:Vocabulary]] Vocabulary
[[Catégorie:Apprentissage profond]] Apprentissage profond
[[Catégorie:Apprentissage profond]] Apprentissage profond
   
   

Version du 26 février 2018 à 21:23

Domaine

Vocabulary Apprentissage profond

Définition

Termes privilégiés

Anglais

Dropout

Dropout is a regularization technique for Neural Networks that prevents overfitting. It prevents neurons from co-adapting by randomly setting a fraction of them to 0 at each training iteration. Dropout can be interpreted in various ways, such as randomly sampling from an exponential number of different networks. Dropout layers first gained popularity through their use in CNNs, but have since been applied to other layers, including input embeddings or recurrent networks. • Dropout: A Simple Way to Prevent Neural Networks from Overfitting • Recurrent Neural Network Regularization