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Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. [1] The GRU is like a long short-term memory (LSTM) with a gating mechanism to input or forget certain features, [2] but lacks a context vector or output gate, resulting in fewer parameters than LSTM. [3]

The gated recurrent unit (GRU) (Cho et al., 2014) offered a streamlined version of the LSTM memory cell that often achieves comparable performance but with the advantage of being faster to compute (Chung et al., 2014).

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A Gated Recurrent Unit (GRU) is a type of recurrent neural network (RNN) architecture that is used in the field of deep learning. GRUs are particularly effective for processing sequences of data for tasks like time series prediction, natural language processing , and speech recognition.

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The Gated Recurrent Unit (GRU) is a type of recurrent neural network architecture commonly used in machine learning and natural language processing. In a real-world scenario, the GRU can be applied in language modeling tasks such as predicting the next word in a sentence or generating human-like text.

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