What are some advanced regularization techniques in AI training?

What are some advanced regularization techniques in AI training?

Advanced regularization techniques include dropout, L1/L2 regularization, and data augmentation. Would you like to know more about any specific technique?

Can you explain how dropout works?

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

What is L1 regularization and how does it differ from L2?

L1 regularization adds a penalty equal to the absolute value of the magnitude of coefficients, promoting sparsity. L2 regularization adds a penalty equal to the square of the magnitude, which tends to distribute weights more evenly.

How can data augmentation help in regularization?

Data augmentation increases the diversity of your training dataset by applying transformations like rotation, scaling, and flipping, which helps the model generalize better and reduces overfitting.