Applications of learning algorithms using deep neural networks have developed considerably recently, often with spectacular results. The physics of complex quantum systems is no exception, with multiple applications that constitute a new field of research. Examples include the representation and optimization of wave functions of quantum systems with large numbers of degrees of freedom, the determination of wave functions from measurements (quantum tomography), and applications to the electronic structure of materials, such as the determination of more precise density functionals or the learning of force fields to accelerate molecular dynamics simulations. This year's lecture will provide an introduction to this field for non-specialists. This introductory lecture will be supplemented by seminars presenting recent developments and current research.