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

Defines weight pruning and its tradeoff, but does not detail pruning algorithms or implementation steps.

Pruning

As explained in Deep Learning with R, Third Edition

Pruning is the removal of selected model parameters or computational structures to reduce model size or computation.

Pruning removes less significant coefficients from weight tensors, reducing a model’s parameter count, memory use, and computation with a potentially small performance cost. section 6.3.2

Use pruning when deploying inference models on smartphones, embedded devices, or low-latency applications. Adjust its level to balance model size and accuracy before exporting or importing the model for deployment. section 6.3.2

How Deep Learning with R, Third Edition covers it

Cover of Deep Learning with R, 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 Python, Third EditionA model-compression technique that removes less important weights or connections to reduce model size and cost.

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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 R, Third Edition. The section links point to the parts of the book it draws on.