Image registration is the process of finding a transformation that aligns one image to another. DIPY currently supports several numerical optimization-based techniques for image registration. Even though these methods perform well, they are limited by their slow registration speeds. The goal of this project is to develop deep learning-based methods that can achieve image registration in one-shot resulting in much faster registration speeds. In this project, I propose to develop deep neural networks (DNNs) for MRI registration using thin-plate splines, free-form deformations, and affine transformations. I also plan to extend the implementation of thin-plate splines to use cases other than image registration.