What are some regularization techniques used in deep learning, and how do they help prevent overfitting?

Regularization techniques are methods used to prevent a model from fitting too closely to the training data, which can lead to overfitting. Common techniques include L1 and L2 regularization, which add a penalty term to the loss function based on the magnitude of the weights. L1 regularization encourages sparsity in the model, potentially leading to feature selection, while L2 regularization penalizes large weights more heavily and can lead to smaller weights overall. Other techniques include adding noise to inputs or weights, using early stopping during training, and employing data augmentation to artificially expand the training dataset, which improves generalization.