recurrent layers
Recurrent layers refer to a type of neural network structure where the output at each step is dependent not only on the current input, but also on the previous output. These layers process sequential data by maintaining an internal state, allowing the network to remember the past information and potentially capture long-term dependencies within the sequence.
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Similar Concepts
- autoencoder layers
- boundary layers
- convolutional layers
- dropout layers
- embedding layers
- fully connected layers
- gated recurrent unit (gru) layers
- multilayer perceptrons
- pooling layers
- recurrent neural networks
- recurrent neural networks (rnn)
- recurrent neural networks with attention
- residual layers
- softmax layers
- transformer layers