Abstract: 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, & GANs.
Attendees will leave with a better understanding of the PyTorch framework. In particular, how it differs from Keras and Tensorflow. Furthermore, a link to a clean documented GitHub repo with the solutions of the examples covered will be provided.
Bio: Robert loves to break deep technical concepts down to be as simple as possible, but no simpler.
Robert has data science experience in companies both large and small. He is currently Head of Data Science for Podium Education, where he builds models to improve student outcomes, and an Adjunct Professor at Santa Clara University’s Leavey School of Business. Prior to Podium Education, he was a Senior Data Scientist at Metis teaching Data Science and Machine Learning. At Intel, he tackled problems in data center optimization using cluster analysis, enriched market sizing models by implementing sentiment analysis from social media feeds, and improved data-driven decision making in one of the top 5 global supply chains. At Tamr, he built models to unify large amounts of messy data across multiple silos for some of the largest corporations in the world. He earned a PhD in Applied Mathematics from Arizona State University where his research spanned image reconstruction, dynamical systems, mathematical epidemiology and oncology.