gated recurrent unit (gru) layers
Gated Recurrent Unit (GRU) layers are a type of recurrent neural network (RNN) layer that help capture and process sequential data efficiently. They use gating mechanisms to control the flow of information within the network, allowing them to selectively remember and forget information from previous timesteps. GRU layers are useful for tasks such as natural language processing, time series analysis, and speech recognition because they can learn long-term dependencies in sequential data while overcoming the vanishing gradient problem typically faced by traditional RNNs.
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