What are some effective regularization techniques in deep learning?

What are some effective regularization techniques in deep learning?

Some effective regularization techniques include L1 and L2 regularization, dropout, and early stopping.

Can you explain how dropout works?

Dropout randomly sets a fraction of the input units to zero during training, which helps prevent overfitting.

What is the difference between L1 and L2 regularization?

L1 regularization adds the absolute value of the weights to the loss function, promoting sparsity, while L2 adds the squared weights, promoting weight decay.

When should I use early stopping?

Early stopping is useful when you want to prevent overfitting by monitoring the model's performance on a validation set and stopping training when performance starts to degrade.