The development of the last years shows that machine learning and especially the subarea of Deep Learning will be an important component in the future, both in the scientific and industrial area. In the BMBF project »Deep Topology Learning« we are working together with other institutes on the acceleration of design algorithms.
From speech recognition and automatic image analysis to prototypes of automatically moving cars or »Go« playing algorithms at world champion level: so-called deep learning processes are almost always behind the success stories. This family of learning methods typically uses overparameterized and usually very large artificial neural networks (DNN) to model the learning problems. The training of such networks requires not only very large amounts of data, but also enormous computing power. Despite the sometimes impressive results achieved with DNNs, they still have some disadvantages that currently often still hinder their broad use in practice: In addition to the typically required very large amounts of data, this is primarily the complex development process.