In this talk I will present a data-driven iteratively regularized Landweber iteration for solving linear and nonlinear ill-posed inverse problems. The method takes into account training data, which are used to estimate the interior of a black box, which is used to define the iteration process. I will show convergence and stability results for the scheme in the infinite dimensional Hilbert spaces and then I will discuss some numerical experiments.