# NanoNets: Deep Learning with 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.

When working on a problem specific to your domain, often the amount of data needed to build models of this size is impossible to find. However models trained on one task capture relations in the data type and can easily be reused for different problems in the same domain. This technique is referred to as Transfer Learning.