Data Augmentation is the act of augmenting the training dataset through transformations that keep the semantic content of the data. For example: in image classification, small geometric transformations (such as rotation by a small angle) do not change the class of the object in the image. Data augmentation is most often used in modeling tasks that require large amounts of data and when such data is difficult to obtain.