Are you interested in applying deep learning to your science problem? Would you like to learn more about the capabilities of deep learning and how to use it in high performance computing environments, and connect with other scientists who are working on similar problems? We are organizing the second Deep Learning for Science School to bring people of similar interests together.
About
Hosted by Computing Sciences at Berkeley Lab, the school brings together researchers and engineers for lectures and tutorials on state-of-the-art deep learning methods and best practices for running deep learning on high performance computing systems. The sessions will cover both theory and practice, with emphasis on the latter. Attendees will gain an understanding of: what deep learning is, what type of problems it is good for, and how to choose, build and train (and deploy) at scale deep learning models for scientific applications. The school will also provide ample opportunities for attendees to connect with fellow scientists with a shared interest for discussions on how the latest advances in learning algorithms can be used for their science.
Who should apply?
The Department of Energy, Office of Science community; domain scientists and engineers, postdocs and graduate students at Universities or National Labs with a strong interest in applying deep learning to scientific problems on high performance computing systems.
Application deadline: 3/4/2020
For more information and applications, visit: