Training & Optimization

L1 Regularization

Adding the sum of absolute weights to the loss function, promoting sparsity and feature selection.

  • Regularization: Explore how Regularization relates to L1 Regularization
  • L2 Regularization: Explore how L2 Regularization relates to L1 Regularization
  • Lasso: Explore how Lasso relates to L1 Regularization
  • Sparsity: Explore how Sparsity relates to L1 Regularization

Why It Matters

Understanding L1 Regularization is crucial for anyone working with training & optimization. This concept helps build a foundation for more advanced topics in AI and machine learning.

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This term is part of the comprehensive AI/ML glossary. Explore related terms to deepen your understanding of this interconnected field.

Tags

training-optimization regularization l2-regularization lasso

Related Terms

L2 Regularization

Adding the sum of squared weights to the loss function, penalizing large weights and improving generalization.

Regularization

Techniques to prevent overfitting by adding constraints or penalties to the model (L1, L2, dropout, early stopping).

Added: November 18, 2025

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