What is data augmentation and why is it used in deep learning?

Data augmentation refers to various techniques used to artificially expand the size of a training dataset by creating modified versions of the existing data. This can include operations like rotation, translation, flipping, scaling, and color adjustments in the context of images, or noise addition and time stretching for audio data. The primary purpose of data augmentation is to improve the generalization capability of neural networks by exposing them to a more diverse set of examples, thus helping to prevent overfitting and making the model more robust to variations in real-world data.