data-augmentation

Data augmentation involves creating new training samples from existing data by applying various transformations such as rotation, scaling, flipping, or adding noise. This technique helps improve the robustness of machine learning models by providing more varied data, which can lead to better generalization on unseen datasets. It is particularly popular in image processing and natural language processing tasks.