Speaker
David Barrett
Description
Throughout the last decade, deep learning has provided us with state-of-the-art results across a wide variety of disciplines, from image recognition and game-play through to protein folding and physics. The connection to physics is particularly interesting since deep learning can be used for physics, and visa-versa, we can use ideas from physics to improve our understanding of deep learning. In this talk, we will derive equations of motion for deep learning using backward error analysis, we will use dynamical systems theory to analyse these equations and we will review recent applications of deep learning in physics and beyond.
