« Extinction de neurone » : différence entre les versions


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== Domaine ==
== Domaine ==


[[category:Vocabulary]]  Vocabulary
[[category:Vocabulary]]  Vocabulary<br />
 
[[Catégorie:Apprentissage profond]] Apprentissage profond
[[Catégorie:Apprentissage profond]] Apprentissage profond
 
== Définition ==
== Définition ==
   
   

Version du 22 mars 2018 à 21:20

Domaine

Vocabulary
Apprentissage profond

Définition

Termes privilégiés

Point mort

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