Presentation
Deep Learning at Scale
Event Type
Tutorial
AI/Machine Learning/Deep Learning
HPC workflows
Parallel Applications
TimeSunday, June 16th9am - 6pm CEST
LocationMatterhorn 2
DescriptionDeep learning is rapidly and fundamentally transforming the way science and industry use data to solve challenging problems. Deep neural network models have been shown to be powerful tools for extracting insights from data across a large number of domains. As these models grow in complexity to solve increasingly challenging problems with larger and larger datasets, the need for scalable methods and software to train them grows accordingly.
This tutorial will continue and expand upon our well attended tutorial at Supercomputing 18 and aims to provide attendees with a working knowledge on deep learning on HPC class systems, including core concepts, scientific applications, and techniques for scaling. We will provide training accounts and example Jupyter notebook-based exercises, as well as datasets, to allow attendees to experiment hands-on with training, inference, and scaling of deep neural network machine learning models.
This tutorial will continue and expand upon our well attended tutorial at Supercomputing 18 and aims to provide attendees with a working knowledge on deep learning on HPC class systems, including core concepts, scientific applications, and techniques for scaling. We will provide training accounts and example Jupyter notebook-based exercises, as well as datasets, to allow attendees to experiment hands-on with training, inference, and scaling of deep neural network machine learning models.
Content Level
Target Audience
Prerequisites
Authors
