Lars Ruthotto: Deep Neural Networks Motivated By Differential Equations (Part 1/2)

Watch part 2/2 here: Machine Learning for Physics and the Physics of Learning Tutorials 2019 “Deep Neural Networks Motivated By Differential Equations (Part 1/2)“ Lars Ruthotto, Emory University Abstract: In this short course, we establish the connection between residual neural networks and differential equations. We will use this interpretation to relate learning problems in data science to optimal control and parameter estimation problems in physics, engineering, and image