Abstract: 

Deep learning is the technology driving today's artificial intelligence boom. It is particularly good at image classification, for instance, deciding whether a picture contains a cat. To get the best results, it's helpful to understand how they work. We will take a gentle, detailed tour though a multilayer fully-connected neural network, backpropagation and a convolutional neural network. You won't need any background in math, programming or machine learning. At Facebook we use deep neural networks as part of our effort to connect the entire world. To provide network connectivity to everyone, we first have to know where everyone is. We use deep learning to find buildings in satellite images so that we know which network technologies will work best and where to deploy them.

Bio: 

I love solving puzzles and building things. Practicing data science gives me the opportunity to do both in equal measure. Like most data scientists, I came to the field indirectly. I started by studying robotics and human rehabilitation at MIT (MS '99, PhD '02), moved on to machine vision and machine learning at Sandia National Laboratories, then to predictive modeling of agriculture DuPont Pioneer, to cloud data science at Microsoft, and finally to satellite image processing at Facebook. In my spare time I like to rock climb, write robot learning algorithms, and go on walks with my wife and our dog, Reign of Terror.