What are advanced regularization techniques in machine learning?

What are advanced regularization techniques in machine learning?

Advanced regularization techniques help prevent overfitting by adding constraints to the model. Examples include L1, L2, and dropout.

Can you explain L1 and L2 regularization in more detail?

Sure! L1 regularization adds a penalty equal to the absolute value of the coefficients, promoting sparsity. L2 regularization adds a penalty equal to the square of the coefficients, which helps in reducing model complexity.

What is dropout and how does it work?

Dropout is a technique where randomly selected neurons are ignored during training. This prevents the model from becoming too reliant on any one neuron, promoting better generalization.

Are there any other techniques I should know about?

Yes, techniques like early stopping, data augmentation, and ensemble methods are also effective in regularization.