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Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Deep learning: Assessing analytics project feasibility and requirements (sponsored by NVIDIA)

Ward Eldred (NVIDIA)
11:20am–12:00pm Wednesday, 09/12/2018
Average rating: *****
(5.00, 2 ratings)

What you'll learn

  • Understand the types of analytical problems that can be solved using deep learning
  • Learn a set of heuristics that can be used to evaluate the feasibility of analytical AI projects

Description

Artificial intelligence (AI) is solving problems that seemed well beyond our reach just a few years back. Using deep learning, the fastest growing segment of AI, computers are now able to learn and recognize patterns from data that were considered too complex for expert-written software. Today, AI deep learning is transforming every industry, including automotive, healthcare, retail, and financial services.

Ward Eldred offers an overview of the types of analytical problems that can be solved using deep learning and shares a set of heuristics that can be used to evaluate the feasibility of analytical AI projects. Ward then covers the computational profile of the deep learning workload and the infrastructure components that need to be set in place to fuel the successful deep learning training process, leaving you with the key tools you need to initiate an analytical deep learning project.

This session is sponsored by NVIDIA.

Photo of Ward Eldred

Ward Eldred

NVIDIA

Ward Eldred is a solution architect responsible for assisting customers in tackling complex business problems with deep learning and HPC solutions that leverage NVIDIA technologies. Ward also leads courses as part of NVIDIA’s Deep Learning Institute, which focuses on teaching students the fundamentals of deep learning through seminars and labs. Previously, Ward spent 20 years as a systems engineer at Sun Microsystems, architecting HA and cluster solutions.