Training & Learning
Regularization
Techniques that prevent models from overfitting training data by penalizing complexity or introducing noise.
Regularization is any technique that reduces overfitting by encouraging the model to learn simpler, more generalizable patterns. Without regularization, large models tend to memorize the training data and fail on new examples.
Common methods include L1/L2 weight penalties (adding a cost for large weights), dropout (randomly disabling neurons during training), early stopping, and data augmentation.
Why it works: constraining the model forces it to rely on robust patterns rather than memorizing training-set quirks.
Regularization is essential for training deep networks with millions or billions of parameters. Different techniques work better for different architectures and tasks.