« Extinction de neurone » : différence entre les versions
mAucun résumé des modifications Balise : Éditeur de wikicode 2017 |
(Ajustement de quelques sections de la fiche.) Balise : Éditeur de wikicode 2017 |
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==Domaine== | ==Domaine== | ||
[[Category: | [[Category:Vocabulaire]]<br/> | ||
[[Category:Intelligence artificielle]]Intelligence artificielle<br/> | |||
[[Category:Apprentissage automatique]] Apprentissage automatique<br/> | |||
[[Category:Apprentissage profond]] Apprentissage profond<br/> | |||
[[Category:Coulombe]]<br/> | |||
[[Category:Termino 2019]] | |||
[[Category:Scotty]] | |||
==Définition== | ==Définition== | ||
Technique de régularisation dont le principe est de désactiver aléatoirement à chaque itération un certain pourcentage des neurones d’une couche afin d'éviter le surajustement. | |||
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==Français== | ==Français== | ||
'''extinction de neurone''' | |||
<poll> | <poll> | ||
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==Anglais== | ==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 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. | ||
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Version du 3 juin 2019 à 19:30
Domaine
Intelligence artificielle
Apprentissage automatique
Apprentissage profond
Définition
Technique de régularisation dont le principe est de désactiver aléatoirement à chaque itération un certain pourcentage des neurones d’une couche afin d'éviter le surajustement.
Français
extinction de neurone
<poll>
Choisissez parmi ces termes proposés :
extinction de neurone
maille de réseau
point mort
trou de neurone
trou de mémoire
trou de réseau
</poll>
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.
Contributeurs: Claude Coulombe, Jacques Barolet, Julie Roy, Patrick Drouin, wiki