How deeply does Grokking Machine Learning cover recurrent neural network?

Defines the architecture and its language-processing role but stops short of explaining its internal equations or training procedure.

Recurrent neural network

As explained in Grokking Machine Learning

A recurrent neural network is a neural network architecture that processes sequences by retaining and updating state across successive inputs.

How can a neural network read a sentence one part at a time while carrying information forward? A recurrent neural network (RNN) is designed for sequences: it processes successive inputs while retaining and updating state. This makes it useful when earlier words or signals matter to later decisions. section 10.5

RNNs are especially associated with natural language processing, where systems analyze language and respond or act. They support applications such as sentiment analysis, machine translation, speech recognition, text summarization, and chatbots. section 10.5

Related sequence-processing architectures include:

Long short-term memory networks (LSTMs), described as a more advanced version of recurrent neural networks. section 10.5
Gated recurrent units (GRUs), another more advanced architecture for processing text. section 10.5

How Grokking Machine Learning covers it

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Also explained in

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Natural Language Processing in Action, Second EditionA recurrent neural network is a neural network architecture that processes sequences while maintaining a state influenced by earlier inputs.
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Sutskever's ListA recurrent neural network is a neural network architecture that processes sequences by maintaining a state updated across successive inputs.
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Deep Learning with Python, Third EditionA neural network architecture that processes sequences by feeding information from one step to the next.
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Deep Learning with R, Third EditionA recurrent neural network is a neural-network model that processes sequences recurrently by carrying information through an internal state across positions.
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Inside Deep Learning: Math, Algorithms, ModelsA neural network that processes sequential data by reusing parameters across time steps and carrying a hidden state forward.
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Graph Neural Networks in ActionA recurrent neural network is a neural network architecture that processes sequences using recurrent state updates.
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Learn Generative AI with PyTorchA neural network architecture for sequential data that processes inputs with loops so information can persist across time steps.
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LLMs in ProductionA recurrent neural network is a neural network that processes ordered inputs while maintaining a state that carries information across sequence positions.
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Data Without LabelsA recurrent neural network is a neural-network architecture designed to model sequences through recurrent state or connections.

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Related concepts

LSTMincludes · An LSTM is a recurrent neural network architecture with gated memory mechanisms that control information flow and help retain or update information across sequences.
sequenceuses · A sequence is an ordered collection of elements whose positions determine their order of traversal or processing.
language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
hidden stateuses · A hidden state is an internal numerical representation in a neural model that encodes information processed up to a particular position, step, or layer.
bidirectional rnnused by · A bidirectional recurrent neural network processes a sequence in forward and reverse directions and combines the resulting representations.
statehas part · State is information or a configuration describing the condition of a system, entity, or computation at a particular time and potentially influencing subsequent behavior or decisions.

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.