We give the first dimension-efficient algorithms for learning Rectified Linear Units (ReLUs), which are functions of the form x -> max(0, w · x) with w ∈ S n−1 . Our algorithm works in the challenging Reliable Agnostic learning model of Kalai, Kanade, and Mansour  where the learner is given access to a distribution D on labeled examples but the labeling may be arbitrary. We construct a hypothesis that simultaneously minimizes the false-positive rate and the loss on inputs given positive labels by D, for any convex, bounded, and Lipschitz loss function. The algorithm runs in polynomial-time (in n) with respect to any distribution on S n−1 (the unit sphere in n dimensions) and for any error parameter ǫ = Ω(1/ log n) (this yields a PTAS for a question raised by F. Bach on the complexity of maximizing ReLUs). These results are in contrast to known efficient algorithms for reliably learning linear threshold functions, where ǫ must be Ω(1) and strong assumptions are required on the marginal distribution. We can compose our results to obtain the first set of efficient algorithms for learning constant-depth networks of ReLUs. Our techniques combine kernel methods and polynomial approximations with a \“dual-loss\” approach to convex programming. As a byproduct we obtain a number of applications including the first set of efficient algorithms for \“convex piecewise-linear fitting\” and the first efficient algorithms for noisy polynomial reconstruction of low-weight polynomials on the unit sphere.