« Réseau récurrent à portes » : différence entre les versions
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[[Category:Vocabulary]] Vocabulary<br /> | [[Category:Vocabulary]] Vocabulary<br /> | ||
[[Category:Claude]]Claude<br /> | |||
[[Catégorie:Apprentissage profond]] Apprentissage profond | [[Catégorie:Apprentissage profond]] Apprentissage profond | ||
== Définition == | == Définition == | ||
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== Termes privilégiés == | == Termes privilégiés == | ||
=== | ===réseau récurrent à portes=== | ||
=== réseau de neurones récurrents à portes=== | |||
===unités récurrentes à porte=== | |||
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== Anglais == | == Anglais == | ||
===Gated Recurrent Unit=== | |||
===GRU=== | |||
The Gated Recurrent Unit is a simplified version of an LSTM unit with fewer parameters. Just like an LSTM cell, it uses a gating mechanism to allow RNNs to efficiently learn long-range dependency by preventing the vanishing gradient problem. The GRU consists of a reset and update gate that determine which part of the old memory to keep vs. update with new values at the current time step. | The Gated Recurrent Unit is a simplified version of an LSTM unit with fewer parameters. Just like an LSTM cell, it uses a gating mechanism to allow RNNs to efficiently learn long-range dependency by preventing the vanishing gradient problem. The GRU consists of a reset and update gate that determine which part of the old memory to keep vs. update with new values at the current time step. | ||
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation | • Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation | ||
• Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano | • Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano | ||
Version du 28 mars 2018 à 14:34
Domaine
Vocabulary
Claude
Apprentissage profond
Définition
Termes privilégiés
réseau récurrent à portes
réseau de neurones récurrents à portes
unités récurrentes à porte
Anglais
Gated Recurrent Unit
GRU
The Gated Recurrent Unit is a simplified version of an LSTM unit with fewer parameters. Just like an LSTM cell, it uses a gating mechanism to allow RNNs to efficiently learn long-range dependency by preventing the vanishing gradient problem. The GRU consists of a reset and update gate that determine which part of the old memory to keep vs. update with new values at the current time step. • Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation • Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano
Contributeurs: Claude Coulombe, Jacques Barolet, Patrick Drouin, wiki
