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gated recurrent unit (GRU)

A Gated Recurrent Unit (GRU) is a simplified version of Long Short-Term Memory (LSTM) designed to solve the same problem of learning long-term dependencies in sequential data but with a more streamlined architecture. GRUs merge the cell state and hidden state used in LSTMs into a single state and utilize two gates—reset and update gates—instead of the three gates (input, output, and forget gates) found in LSTMs. This simplification results in a model that has fewer parameters, making GRUs generally faster to train and less complex, while still retaining the ability to capture long-term dependencies effectively[1][2][3].

Gated Recurrent Units (GRUs) excel at processing sequential data, making them particularly useful for tasks like natural language processing, speech recognition, and time-series analysis.

GRUs were developed to address the vanishing gradient problem often encountered in traditional recurrent neural networks (RNNs). The update gate in a GRU helps the model to decide how much of the past information to carry forward to the future, while the reset gate allows the model to forget the information that is no longer necessary. These mechanisms allow the network to retain information over long sequences, making them more effective for tasks that require long-term dependencies[2][4].

The GRU simplifies the LSTM (Long Short-Term Memory) architecture by combining the forget and input gates into a single “update gate” and merging the cell state and hidden state. This results in a model with fewer parameters than LSTM, making it computationally more efficient and easier to train[1][2]. This makes GRUs computationally more efficient and often faster to train, although they may not always capture long-term dependencies as effectively as LSTMs[2][4].

Compare with: I

Citations:

[1] https://en.wikipedia.org/wiki/Gated_recurrent_unit

[2] https://www.shiksha.com/online-courses/articles/rnn-vs-gru-vs-lstm/

[3] https://www.linkedin.com/advice/0/what-some-common-applications-lstm-gru-ai-ml-skills-neural-networks

[4] https://www.geeksforgeeks.org/gated-recurrent-unit-networks/

[5] https://towardsdatascience.com/understanding-rnns-lstms-and-grus-ed62eb584d90

[6] https://spotintelligence.com/2023/01/30/gated-recurrent-unit-gru/

[7] https://towardsdatascience.com/understanding-gru-networks-2ef37df6c9be

[8] https://datascience.stackexchange.com/questions/14581/when-to-use-gru-over-lstm

[9] https://www.linkedin.com/pulse/unlocking-power-grus-language-modeling-james-ross

[10] https://d2l.ai/chapter_recurrent-modern/gru.html