How deeply does Deep Learning with Python, Third Edition cover beam search?

Defines fixed-width, stepwise exploration but stops short of implementation details or a worked beam-search example.

Beam search

As explained in Deep Learning with Python, Third Edition

A decoding method that keeps a fixed number of candidate generation paths and expands them step by step.

Beam search is a decoding method for text generation that heuristically explores several chains of predicted tokens instead of following only one. At each timestep, it keeps a fixed number of beams and expands those candidate paths. section 16.2.4

Use beam search when generation quality depends on exploring multiple likely continuations.

Keep a fixed number of candidate token chains active at every timestep. section 16.2.4
Treat it as an alternative decoding strategy alongside greedy search, random sampling, and top-K sampling. section 16.2.4

How Deep Learning with Python, Third Edition covers it

Cover of Deep Learning with Python, Third Edition

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Also explained in

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Natural Language Processing in Action, Second EditionBeam search generates sequences by retaining a limited set of high-scoring partial paths and expanding them over multiple decoding steps.
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Transformers in ActionBeam search is a decoding algorithm that maintains a fixed number of highest-probability partial token sequences and expands each sequence at every step.
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Optimization Algorithms: AI techniques for design, planning, and control problemsAn informed search technique that retains only a limited number of the most promising partial solutions at each step.
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Sutskever's ListBeam search is a sequence-decoding algorithm that retains a fixed-size set of high-scoring partial candidates at each expansion step.

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Related concepts

greedy algorithmalternative to · A greedy algorithm builds a solution incrementally by choosing the locally best available option at each step, without generally accounting for all future consequences or revising prior choices.
informed searchkind of · Informed search is a search strategy that uses problem-specific knowledge or heuristic estimates to prioritize promising states or paths.
decoding strategykind of · A decoding strategy is a procedure for selecting successive tokens from a model’s output distribution to generate an output sequence.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with Python, Third Edition. The section links point to the parts of the book it draws on.