One approach to increasing the accuracy in a model regardless of the amount of data you have is to create artificial examples based on existing data. This is called data augmentation. Data augmentation can also be used at test time to improve prediction accuracy.
Data augmentation
Using data augmentation to increase the training data
We are going to apply data augmentation to the MNIST dataset that we used in previous chapters. The code for this section is in Chapter6/explore.Rmd if you want to follow along. In Chapter 5, Image Classification Using Convolutional Neural Networks, we plotted some examples from the MNIST data, so we won't repeat the code again. It is included in the code file, and you can also refer back...
