Let's improve upon our regularized CNN model by adding in more data using a proper image augmentation strategy. Since our previous model was trained on the same small sample of data points each time, it wasn't able to generalize well and ended up overfitting after a few epochs.
The idea behind image augmentation is that we follow a set process of taking in existing images from our training dataset and applying some image transformation operations to them, such as rotation, shearing, translation, zooming, and so on, to produce new, altered versions of existing images. Due to these random transformations, we don't get the same images each time, and we will leverage Python generators to feed in these new ...
