Over the past couple of years, PyTorch has been increasing in popularity in the Deep Learning community. What was initially a tool for Deep Learning researchers has been making headway in industry settings.

In this session, we will cover how to create Deep Neural Networks using the PyTorch framework on a variety of examples. The material will range from beginner - understanding what is going on "under the hood", coding the layers of our networks, and implementing backpropagation - to more advanced material on RNNs, CNNs, LSTMs, & GRUs. Time permitting, we may also spend some time covering the fast.ai library that also uses PyTorch.

Attendees will leave with a better understanding of the PyTorch framework. In particular, how it differs from Tensorflow. Furthermore, a link to a clean documented GitHub repo with the solutions of the examples covered will be provided.