DescriptionDeep learning is rapidly and fundamentally transforming the way science and industry use data to solve 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.
The Deep Learning at Scale tutorial aims to provide attendees with a working knowledge of 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.