How deeply does Machine Learning Algorithms in Depth cover weight decay?

Defines the penalty’s purpose and parameter scope but stops short of deriving its update rule or implementation details.

Weight decay

As explained in Machine Learning Algorithms in Depth

A regularization method that adds a penalty proportional to the squared magnitude of model parameters to discourage large weights.

Weight decay is a regularization method that penalizes the squared magnitude of model parameters, discouraging large weights and promoting simpler models. In multilayer perceptrons, apply it to nonbias weights W rather than bias parameters b to help prevent overfitting. section 10.1

Use weight decay as one option within a broader regularization strategy:

Increase model capacity with additional layers or hidden units when the model underfits. section 10.1
Prefer weight decay when overfitting calls for smaller weights and simpler models. section 10.1
Contrast it with early stopping, which halts training as validation loss rises, and dropout, which randomly removes dense connections. section 10.1

How Machine Learning Algorithms in Depth covers it

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Also explained in

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Grokking Machine LearningRidge regression is a linear regression technique that uses an L2 coefficient penalty, shrinking coefficients toward zero without usually making them zero.
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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIL2 regularization adds a penalty proportional to the squared values of model parameters to constrain model complexity.
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Deep Learning with PyTorch, Second EditionAn optimization method that shrinks parameters in proportion to their current values, generally distinct from L2 regularization for adaptive optimizers.
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Ensemble Methods for Machine LearningL2 regularization penalizes the squared magnitude of model parameters or other model complexity measures in an optimization objective.
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Deep Learning with R, Third EditionL2 regularization is a regularization method that adds a penalty proportional to the squared values of model parameters.
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Deep Learning with Python, Third Edition
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Causal AIL2 regularization is a regularization method that penalizes the sum of squared parameter values.
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Time Series Forecasting Using Foundation ModelsL2 regularization is a penalty method that discourages large model weights by adding their squared magnitudes to the optimization objective.

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Related concepts

regularizationkind of · Regularization is a modeling technique that constrains or penalizes solutions during fitting or optimization to discourage undesirable complexity or behavior and improve generalization or estimation.
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.
ridge regressionused by · Ridge regression is linear regression that applies an L2 penalty to coefficient magnitudes, shrinking estimates toward zero to reduce overfitting.
l2 normuses · The L2 norm is the nonnegative Euclidean magnitude of a vector, equal to the square root of the sum of its components’ squares, and to absolute value for a scalar.
AlexNetused by · AlexNet is a convolutional neural network architecture designed for image classification and recognized for its landmark performance in large-scale image recognition.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Algorithms in Depth. The section links point to the parts of the book it draws on.