Regularization
Techniques to prevent overfitting by adding constraints or penalties to the model (L1, L2, dropout, early stopping).
Related Concepts
- L1 Regularization: Explore how L1 Regularization relates to Regularization
- L2 Regularization: Explore how L2 Regularization relates to Regularization
- Overfitting: Explore how Overfitting relates to Regularization
- Dropout: Explore how Dropout relates to Regularization
Why It Matters
Understanding 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.
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Related Terms
Dropout
A regularization technique that randomly deactivates neurons during training to prevent overfitting and improve generalization.
L1 Regularization
Adding the sum of absolute weights to the loss function, promoting sparsity and feature selection.
L2 Regularization
Adding the sum of squared weights to the loss function, penalizing large weights and improving generalization.
Overfitting
When a model learns training data too well, including noise and outliers, causing poor generalization to new data.