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When given enough training data, machine learning algorithms can do amazing feats. Unfortunately, many applications still struggle to access high-quality data. Making copies of current data and making small modifications to them is one method for increasing the diversity of the training dataset. This is referred to as “data augmentation.” Data augmentation is a low-cost and effective approach to improving the performance and accuracy of machine learning models in data-constrained scenarios. For example:  Let’s suppose your image classification dataset has ten images of cats. You’ve increased the number of cats for the “cat” class by making duplicates of your cat images and turning them horizontally. Rotation, cropping, and translation are some of the additional changes available. You can also combine the changes to increase the number of unique training instances in your collection. The process of changing, or  “augmenting”  a dataset with extra information is known as da...