How deeply does Inside Deep Learning: Math, Algorithms, Models cover GRU?

Explains implementation choices, hidden-state handling, and layer iteration, but defers the GRU's internal gate equations.

GRU

As explained in Inside Deep Learning: Math, Algorithms, Models

A gated recurrent neural network architecture that updates a hidden state over sequence steps with reset and update gates.

A gated recurrent unit (GRU) is a recurrent architecture that makes its hidden state serve as both short- and long-term memory. Compared with an LSTM, it uses less memory, runs faster, and is simpler to implement, although it may be less accurate. section 6.6.2 section 6.6.3

Choose the PyTorch form according to how the sequence is available:

Use nn.GRU when all time steps are available together; it processes a sequence tensor in one operation and supports bidirectional encoding. section 11.4.1
Use nn.GRUCell when inputs arrive one item at a time or output length is unknown; manually preserve hidden states and iterate over steps. section 7.5.1 section 11.4.1

Character-level autoregression

Embed each character, initialize one zero hidden state per GRU layer, and iterate through the sequence one character at a time. Feed every resulting hidden state through a prediction head to estimate the next character, then sum cross-entropy across time steps. Gradient clipping helps stabilize training.

How Inside Deep Learning: Math, Algorithms, Models covers it

Cover of Inside Deep Learning: Math, Algorithms, Models

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Also explained in

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

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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 Inside Deep Learning: Math, Algorithms, Models. The section links point to the parts of the book it draws on.