Glossary › Regularization

Regularization

Techniques that reduce model complexity to improve generalization. L2 (Ridge): penalizes large weights, drives them toward zero. L1 (Lasso): drives some weights to exactly zero, producing sparse models. Dropout: stochastically removes neurons during training. Data augmentation: implicit regularization by increasing effective training set size.

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ML FoundationsBEGINNER5 min read

Regularization: L1, L2, and Dropout Without the Formulas

Models that overfit memorize training data instead of learning patterns. Regularization prevents this. This post explains the three main techniques and when to use each.

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AI EngineeringINTERMEDIATE5 min read

Neural Networks Fundamentals

Build intuition and implementation skill for dense networks, optimization, and regularization.

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