Anyone who is interested in deep learning has gotten their hands dirty playing around with Tensorflow, Google's open source deep learning framework. Tensorflow has its benefits like wide scale adoption, deployment on mobile, and support for distributed computing, but it also has a somewhat challenging learning curve, is difficult to debug, and hard to deploy in production. PyTorch is a new deep learning framework that solves a lot of those problems. PyTorch is only in beta, but users are rapidly adopting this modular deep learning framework. PyTorch supports tensor computation and dynamic computation graphs that allow you to change how the network behaves on the fly unlike static graphs that are used in frameworks such as Tensorflow. PyTorch offers modularity which enhances the ability to debug or see within the network and for many, is more intuitive to learn than Tensorflow. This talk will objectively look at PyTorch and why it might be the best fit for your deep learning use case and we'll look at use cases that will showcase why you might want consider using Tensorflow instead.