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A Gated Recurrent Unit (GRU) is a type of recurrent neural network for sequential data.
GRUs address the vanishing gradient problem in traditional RNNs, enhancing memory retention.
The GRU consists of two main gates: the update gate and the reset gate.
The update gate controls how much past information is carried forward in the sequence.
The reset gate determines how much previous information to forget, optimizing learning.
GRUs are faster and require less memory compared to Long Short-Term Memory (LSTM) networks.
GRUs are widely used in natural language processing, speech recognition, and time series analysis.