R2D2 (Residual-to-Residual DNN series for high-Dynamic range imaging) performs synthesis imaging for radio interferometry. The R2D2 algorithm takes a hybrid structure between a Plug-and-Play (PnP) algorithm and a learned version of the well-known Matching Pursuit algorithm. Its reconstruction is formed as a series of residual images, iteratively estimated as outputs of iteration-specific Deep Neural Networks (DNNs), each taking the previous iteration’s image estimate and associated back-projected data residual as inputs. The primary application of the R2D2 algorithm is to solve large-scale high-resolution high-dynamic range inverse problems in radio astronomy, more specifically 2D planar monochromatic intensity imaging.