  With new advances in machine learning and in particular powerful learning
libraries, we illustrate some of the new possibilities they enable in terms of
nonlinear system identification. For a large class of hybrid systems, we
explain how these tools allow for identification of complex dynamics using
neural networks. We illustrate the method by examining the performance on a
quad-rotor example.
