Deep learning: Assessing analytics project feasibility and requirements (sponsored by NVIDIA)
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.
| Talk Title | Deep learning: Assessing analytics project feasibility and requirements (sponsored by NVIDIA) |
| Speakers | Ward Eldred (NVIDIA) |
| Conference | Strata Data Conference |
| Conf Tag | Make Data Work |
| Location | New York, New York |
| Date | September 11-13, 2018 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
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.
