What are some examples of regularization techniques in deep learning?

What are some examples of regularization techniques in deep learning?

Some common examples include L1 and L2 regularization, dropout, and early stopping.

Can you explain L1 and L2 regularization?

L1 regularization adds the absolute value of the weights to the loss function, promoting sparsity. L2 regularization adds the square of the weights, which helps to prevent overfitting by penalizing large weights.

What is dropout and how does it work?

Dropout is a technique where randomly selected neurons are ignored during training, which helps to prevent overfitting by ensuring that the model does not rely too heavily on any individual neuron.

How does early stopping help in training?

Early stopping monitors the model's performance on a validation set and stops training when performance starts to degrade, preventing overfitting.