What is the difference between data augmentation and regularization?

What is the difference between data augmentation and regularization?

Data augmentation involves creating new training samples by modifying existing data, while regularization helps prevent overfitting by adding constraints to the model.

Can you give me examples of data augmentation techniques?

Sure! Common techniques include rotation, flipping, scaling, and adding noise to images.

How does regularization work in machine learning?

Regularization works by adding a penalty term to the loss function, which discourages complex models and helps improve generalization.

When should I use data augmentation instead of regularization?

Use data augmentation when you have limited data and want to enhance your dataset. Regularization is useful when your model is overfitting.

Are there any scenarios where I should use both?

Yes, using both can be beneficial, especially in deep learning, where data augmentation increases dataset diversity and regularization controls model complexity.