Regularization in Deep Learning

Deep Learning Regularization

Neural networks need regularization to prevent overfitting.

L2 Regularization

Weight decay: add λ||w||² to loss. Implementation: optimizer weight_decay parameter.

Dropout

Randomly drop neurons during training. Reduces co-adaptation. Use Dropout layer.

rate parameter: fraction to drop. Only during training.

Early Stopping

Monitor validation loss. Stop when it increases. Restore best weights.

patience: epochs to wait. Prevents overfitting automatically.

Data Augmentation

Image: random crops, flips, rotations. Text: back-translation, synonym replacement.

Increases effective training data. Reduces overfitting.

Key Takeaways

1. Dropout randomly drops neurons
2. Early stopping monitors validation
3. Data augmentation increases data