GRU

As explained in Grokking Machine Learning

A gated recurrent unit is a recurrent neural-network unit that uses gates to control the retention and update of sequence state.

How Grokking Machine Learning covers it

How deep it goes

Defines GRUs as advanced recurrent architectures but stops short of explaining their gate equations or internal update procedure.

Examples

Uses sentiment analysis, machine translation, speech recognition, text summarization, and topic-specific chatbots as application examples.

What it assumes

Assumes familiarity with neural networks and recurrent neural networks while introducing GRUs among more advanced sequence architectures.

How it connects

Connects GRUs with recurrent neural networks, long short-term memory networks, and natural language processing.

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Also explained in

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  Inside Deep Learning: Math, Algorithms, ModelsA gated recurrent neural network architecture that updates a hidden state over sequence steps with reset and update gates.
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  Graph Neural Networks in ActionA recurrent neural-network architecture that uses reset and update gates to control information flow through its recurrent state.
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  Deep Learning with Python, Third EditionA recurrent neural network unit designed to process sequences with gated state updates.
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  Deep Learning with R, Third EditionA gated recurrent unit (GRU) is a recurrent neural-network architecture that models sequence dependencies with update and reset mechanisms.
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  Natural Language Processing in Action, Second EditionA gated recurrent unit (GRU) is a recurrent neural-network architecture that uses reset and update gates to control information flow and hidden-state updates.
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  Sutskever's ListA gated recurrent unit is a recurrent neural-network unit that uses learned gates to regulate state updates.

Compare how 7 books explain GRU →

Related concepts

• gated updatehas part · A gated update is a state or representation update in which learned gates control how new information is combined with or replaces existing information.
• reset gatehas part · A reset gate is a GRU mechanism that controls how much of the previous hidden state contributes to computing a candidate state.
• vanishing gradient problemaddresses · The vanishing-gradient problem is a neural-network training difficulty in which gradients shrink toward zero as they propagate backward, making updates to earlier layers or distant sequence steps ineffective.
• LSTMalternative to · An LSTM is a recurrent neural network architecture with gated memory mechanisms that control information flow and help retain or update information across sequences.
• recurrent neural networkaddresses · A recurrent neural network is a neural network that processes sequences by updating an internal state across successive inputs, allowing earlier inputs to influence later computations.
• bidirectional rnncontrasts with · A bidirectional recurrent neural network processes a sequence in forward and reverse directions and combines the resulting representations.

This explainer was generated by Manning's knowledge graph from the text of Grokking Machine Learning. The section links point to the parts of the book it draws on.