Deep Learning for Hobby Robotics

Bennett Wineholt


Recent work to reduce the size and computational requirements of deep neural networks for machine learning has allowed applications including video object recognition and speech recognition to be performed responsively on small robotic systems which are commonly limited by power and payload constraints.  This talk will present an application lifecycle for developing robot behaviors using deep learning techniques as well as describing advances in model compression which make these techniques more performant.

Bio: Bennett Wineholt is a staff member at the Cornell University Center for Advanced Computing supporting faculty needs for computing and consulting services to accelerate discovery.