Difference between revisions of "Gated Recurrent Unit (GRU)"
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a gating mechanism in [[Recurrent Neural Network (RNN)]] The GRU is like a long short-term memory (LSTM) with forget gate[2] but has fewer parameters than LSTM, as it lacks an output gate.[3] GRU's performance on certain tasks of polyphonic music modeling and speech signal modeling was found to be similar to that of LSTM. GRUs have been shown to exhibit even better performance on certain smaller datasets. [http://en.wikipedia.org/wiki/Gated_recurrent_unit Gated Recurrent Unit | Wikipedia] | a gating mechanism in [[Recurrent Neural Network (RNN)]] The GRU is like a long short-term memory (LSTM) with forget gate[2] but has fewer parameters than LSTM, as it lacks an output gate.[3] GRU's performance on certain tasks of polyphonic music modeling and speech signal modeling was found to be similar to that of LSTM. GRUs have been shown to exhibit even better performance on certain smaller datasets. [http://en.wikipedia.org/wiki/Gated_recurrent_unit Gated Recurrent Unit | Wikipedia] | ||
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Revision as of 19:10, 30 June 2019
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- Neural Network Zoo | Fjodor Van Veen
- Long Short-Term Memory (LSTM)
- Recurrent Neural Network (RNN)
- Animated RNN, LSTM and GRU | Raimi Karim - Towards Data Science
a gating mechanism in Recurrent Neural Network (RNN) The GRU is like a long short-term memory (LSTM) with forget gate[2] but has fewer parameters than LSTM, as it lacks an output gate.[3] GRU's performance on certain tasks of polyphonic music modeling and speech signal modeling was found to be similar to that of LSTM. GRUs have been shown to exhibit even better performance on certain smaller datasets. Gated Recurrent Unit | Wikipedia
