How deeply does Grokking Machine Learning cover LSTM?

Introduces LSTM as an advanced RNN architecture but does not detail its internal memory-cell or gating mechanics.

LSTM

As explained in Grokking Machine Learning

A long short-term memory network is a recurrent neural network architecture with gated memory cells designed to learn long-range dependencies in sequences.

An LSTM is an advanced recurrent neural network architecture for processing text and other sequences. Like an RNN, it feeds an output back into the network so later steps can use earlier information. section 10.5

LSTMs support language-focused tasks such as:

Sentiment analysis: determining whether a sentence expresses positive or negative sentiment. section 10.5
Machine translation, speech recognition, text summarization, and topic-specific chatbots. section 10.5

Reading a sentence

When a network analyzes a sentence for sentiment, an LSTM can process the sequence of words while carrying information forward, helping produce a positive-or-negative judgment. section 10.5

How Grokking Machine Learning covers it

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Also explained in

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Deep Learning with Python, Third EditionA long short-term memory recurrent neural network architecture designed to capture long-range dependencies in sequences.
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Deep Learning with R, Third EditionLong short-term memory is a gated recurrent-neural-network architecture designed to preserve and control information across sequence timesteps.
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Inside Deep Learning: Math, Algorithms, ModelsA gated recurrent neural-network architecture designed to learn long-range dependencies.
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Learn Generative AI with PyTorchA recurrent neural network variant with long short-term memory units, mentioned here as an example of a deep learning model that cannot process raw text directly.
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Sutskever's ListLong short-term memory is a recurrent neural network architecture with gated state updates for modeling long-range sequence dependencies.
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Natural Language Processing in Action, Second EditionLong short-term memory is a recurrent neural network architecture that uses gated state updates to preserve and control information over long sequences.
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LLMs in ProductionA long short-term memory network is a recurrent neural network architecture that uses memory cells and gates to preserve or discard information across sequences.

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Related concepts

recurrent neural networkkind of · 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.
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.
carry statehas part · A carry state is an LSTM’s persistent long-term memory representation, transported across sequence positions through selectively gated updates.
GRUalternative to · A gated recurrent unit (GRU) is a recurrent neural-network architecture that uses learned gates to regulate information flow and update a hidden state across sequence steps.
maskused by · Masking is the selective representation, concealment, inclusion, exclusion, or restriction of parts of data or a system according to specified criteria.
embeddingused by · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.

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.