Title: Stochastic Proximal Point Algorithm for Large-scale Nonconvex Optimization: Convergence, Implementation, and Application to Neural Networks. Abstract: We revisit the stochastic proximal point algorithm (SPPA) for large-scale nonconvex optimization problems. SPPA has been shown to converge faster and more stable than the celebrated stochastic gradient descent (SGD) algorithm, and its many variations, for convex problems. However, the per-iteration update of SPPA is defined abstractly and has long been considered expensive. In this paper, we show that efficient implementation of SPPA can be achieved. If the problem is a nonlinear least squares, each iteration of SPPA can be efficiently implemented by Gauss-Newton; with some linear algebra trick the resulting complexity is in the same order of SGD. For more generic problems, SPPA can still be implemented with L-BFGS or accelerated gradient with high efficiency. Another contribution of this work is the convergence of SPPA to a stationary point in expectation for nonconvex problems. The result is encouraging that it admits more flexible choices of the step sizes under similar assumptions. The proposed algorithm is elaborated for both regression and classification problems using different neural network structures. Real data experiments showcase its effectiveness in terms of convergence and accuracy compared to SGD and its variants.