What is AI regularization and why is it important?

What is AI regularization and why is it important?

AI regularization refers to techniques used to prevent overfitting in machine learning models. It helps improve model generalization.

Can you explain some common regularization techniques?

Sure! Common techniques include L1 regularization (Lasso), L2 regularization (Ridge), and dropout in neural networks.

How does L1 regularization differ from L2 regularization?

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

When should I use dropout in my neural network?

Dropout is useful when you have a large neural network and want to prevent overfitting by randomly dropping units during training.