Leveraging Watson's Machine Learning GPUs to accelerate your Deep Learning project in Python

Everyone who's done a Deep Learning project knows how long it takes to train a single model not to mention optimizing it. As a current student, I don't have access to any GPUs which sometimes leads to frustration because I can spend most of my time waiting for the model to see all the training batches. As you might have heard, GPUs can considerably decrease training time when used for Deep Learning projects. In a nutshell, a GPU is a graphics processing unit that is now integrated in most laptops to efficiently manipulate computer graphics and image processing. It has a highly parallel structure that allows it to make more computations in parallel than the usual CPU (central processing unit). This is why GPUs have become the popular kid when it comes to neural networks (NN).