Table of Contents
Regularization methods are essential in deep learning to prevent overfitting and improve model generalization. This article provides an overview of three common techniques: L1, L2, and Dropout.
L1 Regularization
L1 regularization adds a penalty equal to the absolute value of the weights to the loss function. It encourages sparsity in the model by driving some weights to zero, effectively performing feature selection.
L2 Regularization
L2 regularization adds a penalty proportional to the square of the weights. It discourages large weights, leading to smoother models that are less sensitive to fluctuations in the training data.
Dropout
Dropout randomly deactivates a subset of neurons during training, which prevents neurons from co-adapting. This technique helps in reducing overfitting and improves the robustness of the model.
Common Regularization Techniques
- L1 Regularization
- L2 Regularization
- Dropout
- Early Stopping
- Data Augmentation