Difference between revisions of "Gated Recurrent Unit (GRU)"
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* [[Long Short-Term Memory (LSTM)]] | * [[Long Short-Term Memory (LSTM)]] | ||
* [[Recurrent Neural Network (RNN)]] | * [[Recurrent Neural Network (RNN)]] | ||
| + | * [http://towardsdatascience.com/understanding-gru-networks-2ef37df6c9be Understanding GRU Networks | Simeon Kostadinov - Towards Data Science] | ||
* [http://towardsdatascience.com/animated-rnn-lstm-and-gru-ef124d06cf45 Animated RNN, LSTM and GRU | Raimi Karim - Towards Data Science] | * [http://towardsdatascience.com/animated-rnn-lstm-and-gru-ef124d06cf45 Animated RNN, LSTM and GRU | Raimi Karim - Towards Data Science] | ||
Revision as of 19:15, 30 June 2019
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- Neural Network Zoo | Fjodor Van Veen
- Long Short-Term Memory (LSTM)
- Recurrent Neural Network (RNN)
- Understanding GRU Networks | Simeon Kostadinov - Towards Data Science
- 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
