Data augmentation is a technique for increasing the size of a training dataset artificially by producing modified versions of the original dataset. The ability of Neural Network models to generalize what they have learned to new data can be improved by training neural network models on more data, and the augmentation techniques can create variations of the datasets that can improve the ability of the fit models to generalize what they have learned to new unseen data. In this work, we will look at how to improve model robustness by means of data augmentation when training neural networks.