The main goal of Machine Learning (ML) is generalization; that is, we train an algorithm on a training dataset and we want the algorithm to work with high performance (accuracy) on an unseen dataset. In order to solve a complex image processing task (such as image classification), the more training data we have, we may expect better generalization—ability of the ML model learned, provided we have taken care of overfitting (for example, with regularization). But with traditional ML techniques, not only does it become computationally very expensive with huge training data, but also, the learning (improvement in generalization) often stops at a certain point. Also, the traditional ML algorithms often need lots of domain expertise and human intervention and they are only capable of what they are designed for—nothing more and nothing less. This is where deep learning models are very promising.