11-11, 15:30–17:00 (America/New_York), Central Park East (6th floor)
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 CNNs
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 CNNs
No previous knowledge expected
Robert loves to break deep technical concepts down to be as simple as possible.
Robert has data science experience in companies both large and small. He is currently VP of Data Science for Podium Education, where he builds models to improve student outcomes, and an Artificial Intelligence Lead at NASA's Frontier Development Lab. 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.