and in general you want a good model should be invariant to small perturbations on this input data that don't actually change the nature of the example and yeah so data augmentation is a technique to boost your training data size and the diversity of it so the general idea is you're augmenting in some way again adding some noise to your input samples so that you cannot that you can both get more training data and have more diverse training data and I guess what we'll see some examples of what diversity means so yeah basically