The ultimate goal of deep learning is to remove the need to transform input. The traditional computer vision pipeline works like this: you preprocess the image

The ultimate goal of deep learning is to remove the need to transform input. The traditional computer vision pipeline works like this: you preprocess the image, featurize it with features like SIFT, and then run an SVM classifier. However, this pipeline is totally beaten by end-to-end neural networks. Same will hold true for other domains. As long as we've discovered the right architecture, aka. the family for f(x), learning still boils down to training your models.