NVIDIA Deep Learning Institute

NVIDIA Deep Learning Institute
Tuesday, August 20 & Wednesday, August 21, 2019

San Diego Supercomputer Center, UC San Diego 
SDSC Auditorium 


NVIDIA and SDSC are pleased to offer a 2-day course on the Fundamentals of Deep Learning for Computer Vision and Accelerating Data Science Workflows with RAPIDS.

Why you should attend: NVIDIA GPUs are among the world’s fastest and most efficient accelerators delivering world record performance on AI workloads including Deep Learning, Machine Learning, and Data Analytics. NVIDIA provides optimized software stacks to accelerate end-to-end training, inference, and ETL data processing tasks in deep learning and machine learning workflows. Every major AI and deep learning framework is GPU-accelerated, so data scientists and researchers can become productive quickly without any GPU programming experience. 
SDSC will wrap each day with a hands-on lesson running applications on both NVIDIA AWS services and the Comet cluster. 

There is no cost to attend. 
Seating is limited to the first 60 people registered. 

Who it's for: Data Scientists, Academic Professionals, Researchers, Postdocs and Graduate Students

Tuesday, August 20: Fundamentals of Deep Learning for Computer Vision
Course Description
: Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities. In this workshop, you'll learn the basics of deep learning by training and deploying neural networks. You'll learn to:

  • Implement common deep learning workflows, such as image classification and object detection
  • Experiment with data, training parameters, network structure, and other strategies to increase performance and capability
  • Deploy your neural networks to start solving real-world problems
SDSC Session: Utilizing SDSC Comet for Deep Learning
Upon completion, you'll be able to start solving problems on your own with deep learning. 
Prerequisites: Familiarity with basic programming fundamentals such as functions and variables
Framework: Caffe, DIGITS
Assessment Type: Code-based
Certificate Available
Wednesday, August 21: Accelerating Data Science Workflows with RAPIDS 
Course Description:The open source RAPIDS project allows data scientists to GPU-accelerate their data science and data analytics applications from beginning to end, creating possibilities for drastic performance gains and techniques not available through traditional CPU-only workflows.
Learn how to GPU-accelerate your data science applications by:
  • Utilizing key RAPIDS libraries like cuDF (GPU-enabled  Pandas-like dataframes) and cuML (GPU-accelerated machine learning algorithms)
  • Learning techniques and approaches to end-to-end data science, made possible by rapid iteration cycles created by GPU acceleration
  • Understanding key differences between CPU-driven and GPU-driven data science, including API specifics and best practices for refactoring
SDSC Session: Utilizing SDSC Comet for Machine Learning and Data Science
Upon completion, you'll be able to refactor existing CPU-only data science workloads to run much faster on GPUs and write accelerated data science workflows from scratch.
Prerequisites: Intermediate competency in Pandas, NumPy, and scikit-learn
Framework: None