IEEE EMBS Lecture: Prof.Debdoot Sheet, Assistant Professor, Indian Institute of Technology Kharagpur
Deep Learning: What's the buzz all about and harnessing the power of self hierarchical learning
- Speaker: Prof.Debdoot Sheet
- Date: Monday, August 24, 2015
- Time: 6:00 PM
- Location: TUM Main Campus, Room EG.120
Debdoot Sheet is an Assistant Professor of Electrical Engineering at the Indian Institute of Technology Kharagpur and Founder of SkinCurate?
Research, India. He received the B.Tech. degree in electronics and communication engineering in 2008 from the West Bengal University of Technology, Kolkata, MS and PhD?
degrees in computational imaging and machine learning from the Indian Institute of Technology Kharagpur in 2010 and 2014 respectively. His current research interests include computational medical imaging, machine learning, image and multidimensional signal processing, and social implications of technology. He is also a DAAD alumnus and was a visiting scholar at the Technical University of Munich during 2011-12. He has received the IEEE Computer Society Richard E. Merwin Student Scholarship in 2012, the Fraunhofer Applications Award at the Indo-German Grand Science Slam in 2012, Samsung GRO Award in 2013, and winner of the GE Edison Challenge in 2013. He is a member of IEEE, SPIE and ACM and serves as Regional Editor of IEEE Pulse and Editor-in-Chief of IEEE Technology and Engineering Education since 2014.
Deep learning is a genre of machine learning algorithms that attempt to solve tasks by learning abstraction in data following a stratified description paradigm using non-linear transformation architectures. When put in simple terms, say you want to make the machine recognize some Mr. X with Mt. E in the background, this task is a stratified or hierarchical recognition task. At the base of the recognition pyramid would be kernels which can discriminate flats, lines, curves, sharp angles, color; higher up will be kernels which use this information to discriminate body parts, trees, natural scenery, clouds, etc.; higher up will use this knowledge to recognize humans, animals, mountains, etc.; and higher up will learn to recognize Mr. X and Mt. E and finally the apex lexical synthesizer module would say that Mr. X is standing in front of Mt. E. Deep learning is all about how you make machines synthesize this hierarchical logic and also learn these representative kernels all by itself.
Deep learning has been extensively used to efficiently solve and provide state of art solutions to problems like handwritten character recognition, speech recognition, lexical ordered speech synthesis, object and product recognition, image retrieval, content filtering, product visibility tracking, computational medical imaging. This workshop will focus on the buzz around this topic and how firm does the buzz hold on to the claims it boasts of? Also we would host a hands-on tutorial with implementing a deep network using auto encoders for solving the hand-written digit recognition problem.