Deep learning has shown tremendous success in solving various tasks in several fields of science such as in image and natural language processing. Recently, it has also been applied to solve inverse problems and empirical evidence in image reconstruction points to exponential improvements in both performance and run-time over classical approaches. In this talk we outline some of these recent developments. In particular, we’ll introduce ‘Learned Iterative Reconstruction’, a method which relies on a fusion of model- and data-driven approaches for solving inverse problems. We’ll also show how these methods can be extended to ‘Deep Bayesian Inversion’, a family of methods that allows us to perform uncertainty quantification using deep learning.