How to use Deep Learning when you have Limited Data

There has been a recent surge in popularity of Deep Learning, achieving state of the art performance in various tasks like Language Translation, playing Strategy Games and Self Driving Cars requiring millions of data points. One common barrier for using deep learning to solve problems is the amount of data needed to train a model. The requirement of large data arises because of the large number of parameters in the model that machines have to learn. Deep Learning is nothing but Large Neural networks, they can be thought of as a flow chart where data comes in from one side and inference/knowledge comes out the other. You can also break the neural network, pull it apart and take the inference out from wherever you please.