Shruti Jadon

ML Software Engineer at Juniper Networks

Shruti Jadon is currently working as a Machine Learning Software Engineer at Juniper Networks and Visiting Researcher at Rhode Island Hospital(Brown University). She also authored book on “Hands on One-Shot Learning using Python” with Packt Publishing. She has obtained her Masters’ Degree in Computer Science from University of Massachusetts, Amherst. Her research interests include deep learning architectures, computer vision, and convex optimization. In past, she has worked at Autodesk, Quantiphi, SAP Labs, and Snapdeal.

WATCH LIVE: December 9 @ 2:25PM – 2:55PM ET

How to train a Deep Learning Model in low data regime

Humans learn new things with a very small set of examples e.g. a child can generalize the concept of a ”Dog” from a single picture but a machine learning system needs a lot of examples to learn its features. In particular, when presented with stimuli, people seem to be able to understand new concepts quickly and then recognize variations on these concepts in future percepts. Machine learning as a field has been highly successful at a variety of tasks such as classification, web search, image, and speech recognition. Often times, however, these models do not do very well in the regime of low data. In this talk, I will explain what all types of Deep Learning approach we can use to tackle this problem.