Title: Learning large-scale Kernel Networks. Abstract:     This paper concerns large-scale training of *Kernel Networks*, a generalization of kernel machines that allows the model to have arbitrary centers. We propose a scalable training algorithm -- EigenPro 3.0 -- based on alternating projections with preconditioned SGD for the alternating steps. In contrast to classical kernel machines, but similar to neural networks, our algorithm enables decoupling the learned model from the training set. This empowers kernel models to take advantage of modern methodologies in deep learning, such as data augmentation. We demonstrate the promise of EigenPro 3.0 on several experiments over large datasets. We also show data augmentation can improve performance of kernel models.