Deep Learning is really good when dealing with images where conventional machine learning methodologies fell short. Still when training a deep neural network we need a lot of labeled examples unlike a human which can learn an object from even a single image. Collecting labeled images is not only cumbersome but also expensive. Training a classifier with few examples will simply overfit on the training dataset and will not generalise well. All in all in this talk I will cover some of the approaches that can be used to train a Neural network based image classifier when given few examples from different classes. Audience will get to learn the concept of few shot learning, current research trends, common approaches to tackle this problem.