Regularization

As explained in Causal AI

Regularization is a modeling technique that adds a penalty or constraint to an optimization objective to discourage undesirable parameter values.

How Causal AI covers it

How deep it goes

Defines the practice and explains L2’s squared-parameter mechanism, without developing broader regularization methods.

Examples

Uses L2 regularization and the sum of squared parameter values as the concrete implementation example.

What it assumes

Assumes likelihood and loss functions, while defining regularization as an additional loss component.

What you can do after

Build it from scratch by appending a parameter-magnitude penalty to a chosen loss function.

How it connects

Connects regularization to loss functions, maximum likelihood estimation, numerical optimization, parameter estimation, and Bayesian estimation.

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Also explained in

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  Ensemble Methods for Machine LearningA regularization function is a function that assigns a penalty to model parameters or complexity as part of a regularized objective.
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  Grokking Machine LearningRegularization is a model-training technique that penalizes complexity while optimizing predictive performance.
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  Deep Learning with R, Third EditionRegularization is a modeling technique that constrains learning to reduce overfitting and improve generalization to unseen data.
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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIRegularization is a technique that limits model complexity by penalizing parameter values to reduce overfitting and improve generalization.
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  Math and Architectures of Deep LearningThe practice of constraining a learned representation or model to encourage desirable structure such as smoothness or compactness.
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  Deep Learning with Python, Third EditionA method that constrains a model during training to reduce overfitting and improve generalization.
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  Reinforcement Learning from Human FeedbackRegularization is a technique that constrains optimization to reduce undesirable or excessive fitting to an objective.
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  Deep Learning with PyTorch, Second EditionA set of techniques that reduce overfitting and improve generalization by constraining model behavior during training.
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  Sutskever's ListA regularizer is a modeling mechanism that discourages undesirable solutions to improve generalization or control model complexity.
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  Machine Learning Algorithms in DepthA modeling technique that adds a penalty or constraint to discourage undesirable parameter values, such as excessive magnitude.
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  Bayesian Optimization in ActionA loss function quantifies the discrepancy between a model's predictions and desired outcomes or data, with lower values indicating better fit.
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  Machine Learning System DesignRegularization is a technique that constrains model fitting to discourage overly complex solutions and improve generalization.

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Related concepts

• 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.
• dropoutincludes · Dropout is a neural-network regularization technique that randomly suppresses units, connections, or activations during training to reduce overfitting.
• regularization parameterconfigured by · A regularization parameter is a model-fitting setting that controls the influence of a penalty or constraint relative to fit to the data.
• variational autoencoderused by · A variational autoencoder is a generative neural network that learns a probabilistic latent representation of data and decodes samples from it to reconstruct or generate data.
• Kullback–Leibler divergenceuses · Kullback–Leibler divergence is a nonnegative, directed measure of the information lost when one probability distribution approximates another, computed as an expected log ratio under the first distribution.
• marginal likelihoodequivalent to · Marginal likelihood, or Bayesian evidence, is the probability or density of observed data under a model after marginalizing over unknown parameters or latent variables according to their prior distributions.

This explainer was generated by Manning's knowledge graph from the text of Causal AI. The section links point to the parts of the book it draws on.