After taking over natural signal processing by storm, deep learning is being increasingly applied to various other areas in electrical engineering such as communications, smart grid, and material design. For those of you who have wondered if "deep learning" or "artificial intelligence" is right for your applied research, this talk might be for you. The outline will be as follows: 1. Number of deep learning papers in various IEEE Transactions in the last two years. 2. What is different about deep learning compared to other forms of machine learning? 3. Some interesting and diverse case studies of deep learning applications outside of natural signal processing. 4. Demonstration of how easy it is to apply basic deep learning.
Amit Sethi is an Associate Professor of EE at IITB. He works on the applications of deep learning to medical images, image super-resolution, variants and applications of of non-negative matrix factorization, and other problems in image processing and computer vision. He has previously worked at IIT Guwahati and ZS Associates (a management consulting firm). Deepak Anand is a Ph.D. Scholar at Department of Electrical Engineering, IIT Bombay. Deepak Anand works in the area of deep learning applications to the medical images specifically for cancer diagnosis and prognosis. Deepak Anand has worked in the area with pathology, radiology, and clinical images. Deepak Anand also has a keen interest in the field of Control theory, Numeric Linear Algebra and Optimization.