Image Augmentation 

According to fastai and Jeremy Howard, data augmentation is a powerful technique to artificially expand your training dataset by creating modified versions of your existing data.

Jeremy Howard emphasizes several key points about data augmentation:

1. It helps prevent overfitting by forcing the model to learn robust features
2. Different domains require different augmentation strategies (e.g., medical imaging vs. natural photos)
3. Augmentations should reflect real-world variations your model might encounter
4. It's particularly effective when you have limited training data

Data augmentation is applied only to the training set, not the validation or test sets, as we want to evaluate the model on unmodified data that represents real-world conditions.