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Regularization

AI/ML Fundamentals

Techniques to prevent overfitting

What is Regularization?

Methods constraining model complexity: L1/L2 regularization, dropout, early stopping, data augmentation.

Real-World Examples

  • •L2 penalty on weights
  • •Dropout = 0.5
  • •Early stopping when validation loss increases

When to Use This

Essential for preventing overfitting in complex models

Related Terms

Learn more about concepts related to Regularization

Overfitting

Model learns training data too well, performs poorly on new data

Dropout

Regularization technique randomly ignoring neurons

Training

Process of teaching an ML model using data