Data augmentation is the technique of increasing the size of data used for training a model. For reliable predictions, the deep learning models often require a lot of training data, which is not always available. Therefore, the existing data is augmented in order to make a better generalized model.
For example, in case of images, the original image can be transformed using techniques such as flipping, rotation, color jittering etc.
Read the complete post at OpenGenus IQ, written by me as a part of GSSoC.