« Réseau récurrent à portes » : différence entre les versions

Version du 26 février 2018 à 20:34

Domaine

Catégorie Démo Apprentissage profond

Définition

Termes privilégiés

Anglais

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

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 [[catégorie:Démo]] Catégorie Démo
+[[Catégorie:Apprentissage profond]] Apprentissage profond
 == Définition ==
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 == Anglais ==
+'''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