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

Defines coefficient selection and the size–accuracy tradeoff but does not provide pruning code or algorithmic internals.

Pruning

As explained in Deep Learning with Python, Third Edition

A model-compression technique that removes less important weights or connections to reduce model size and cost.

Pruning compresses a trained model by removing less significant weights or connections. It targets deployments constrained by memory, power, or latency, accepting a small performance cost for a smaller model. section 6.3.2

Use pruning as a controllable size–accuracy tradeoff:

Keep the most significant coefficients in each weight tensor and remove the rest. section 6.3.2
Tune the pruning amount: more pruning can reduce memory and compute further, but may reduce performance metrics. section 6.3.2
Optimize before importing the model into TensorFlow.js or exporting it to TensorFlow Lite. section 6.3.2

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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LLMs in ProductionStructured pruning removes whole structural components of a neural network, such as filters, channels, or layers.
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Statistics Every Programmer NeedsPruning is the removal of branches or subtrees from a decision tree to simplify the model and limit unnecessary complexity.
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Advanced Algorithms and Data StructuresPruning is the elimination of search-tree branches that cannot contain a valid or better candidate.
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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIPruning removes selected decision-tree splits after or during construction to reduce tree complexity.
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Grokking AI Algorithms, Second EditionPruning is the removal or restriction of branches in a decision tree to reduce excessive specificity and improve generalization.
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Deep Learning with R, Third EditionPruning is the removal of selected model parameters or computational structures to reduce model size or computation.

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Related concepts

baggingcontrasts with · Bagging, or bootstrap aggregation, is an ensemble technique that trains multiple models on bootstrap samples of data and combines their predictions.
decision treeuses · A decision tree is a branching structure that represents sequential tests or choices, with paths leading to outcomes, predictions, or actions.
overfittingaddresses · Overfitting is a modeling condition in which a model fits patterns or idiosyncrasies in its training data too closely and consequently generalizes poorly to new data.

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.