« Extinction de neurone » : différence entre les versions
Aucun résumé des modifications |
(extinction de neurone à la place de neurone éteint) |
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== Domaine == | ==Domaine== | ||
[[category:Vocabulary]] | [[category:Vocabulary]] | ||
[[Category:Claude]]Claude<br /> | Vocabulary<br /> | ||
[[Catégorie:Apprentissage profond]] Apprentissage profond | [[Category:Claude]] | ||
Claude<br /> | |||
[[Catégorie:Apprentissage profond]] | |||
Apprentissage profond | |||
== Définition == | ==Définition== | ||
injection de bruit pour rendre le réseau plus robuste, équivalent <br /> | injection de bruit pour rendre le réseau plus robuste, équivalent <br /> | ||
<br /> | <br /> | ||
== Français == | ==Français== | ||
<poll> | <poll> | ||
Choisissez parmi ces termes proposés : | Choisissez parmi ces termes proposés : | ||
extinction de neurone | |||
maille de réseau | maille de réseau | ||
point mort | point mort | ||
trou de neurone | |||
trou de mémoire | trou de mémoire | ||
trou de réseau | trou de réseau | ||
</poll> | </poll> | ||
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<br /> | <br /> | ||
== Anglais == | ==Anglais== | ||
===Dropout=== | ===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. | ||
* Dropout: A Simple Way to Prevent Neural Networks from Overfitting | |||
* Recurrent Neural Network Regularization | *Dropout: A Simple Way to Prevent Neural Networks from Overfitting | ||
*Recurrent Neural Network Regularization | |||
<br /> | <br /> | ||
Version du 11 avril 2019 à 17:02
Domaine
Vocabulary
Claude
Apprentissage profond
Définition
injection de bruit pour rendre le réseau plus robuste, équivalent
Français
<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.
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting
- Recurrent Neural Network Regularization
Contributeurs: Claude Coulombe, Jacques Barolet, Julie Roy, Patrick Drouin, wiki
