Deep learning: Assessing analytics project feasibility and computational requirements
Adam Grzywaczewski offers an overview of the types of analytical problems that can be solved using AI and shares a set of heuristics that can be used to evaluate the feasibility of analytical AI projects. Adam then covers the computational requirements for the deep learning training process, leaving you with the key tools you need to initiate an analytical AI project.
| Talk Title | Deep learning: Assessing analytics project feasibility and computational requirements |
| Speakers | Adam Grzywaczewski (NVIDIA) |
| Conference | Strata Data Conference |
| Conf Tag | Making Data Work |
| Location | London, United Kingdom |
| Date | May 23-25, 2017 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
The ability to transport and store energy has had a profound impact on the human race. Artificial intelligence is starting to have a similar effect. No wonder experts like Andre Ng have recently compared deep learning and artificial intelligence to electricity. Adam Grzywaczewski offers an overview of the types of analytical problems that can be solved using AI and shares a set of heuristics that can be used to evaluate the feasibility of analytical AI projects. Adam 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 AI project. This session is sponsored by NVIDIA.
