I will discuss how we go about applying deep learning to our work at NVIDIA, solving problems in a variety of domains from graphics and vision to text and speech. I’ll discuss the properties of successful machine learning applications, the criteria that we use to choose projects, and things to watch out for while creating new deep learning applications. I’ll discuss the role of HPC in helping us conduct our research, and I’ll show some examples of projects that benefit from scale.
More About the Speaker:
Bryan Catanzaro is VP of Applied Deep Learning Research at NVIDIA, where he leads a team solving problems in domains ranging from video games to chip design using deep learning. Bryan earned his PhD from Berkeley, where he focused on parallel computing, machine learning, and programming models.
He earned his MS and BS from Brigham Young University, where he worked on higher radix floating-point representations for FPGAs.
Bryan worked at Baidu to create next generation systems for training and deploying deep learning models for speech recognition. Before that, he was a researcher at NVIDIA, where he worked on programming models for parallel processors, as well as libraries for deep learning, which culminated in the creation of the widely used CUDNN library.