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GTC On-Demand

Presentation
Media
Abstract:
We will highlight the power of hybrid probabilistic deep learning by discussing how this approach is used for building system-of-system models for large-scale systems such as refineries, power generation systems, and gas compression systems. We'll cover how GPUs accelerate all three applications, with a focus on a time series prediction model for predicting overall production in a large oil field with multiple changing parameters.
We will highlight the power of hybrid probabilistic deep learning by discussing how this approach is used for building system-of-system models for large-scale systems such as refineries, power generation systems, and gas compression systems. We'll cover how GPUs accelerate all three applications, with a focus on a time series prediction model for predicting overall production in a large oil field with multiple changing parameters.  Back
 
Topics:
Predictive Analytics for Retail, AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9744
Streaming:
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Abstract:
By 2035, artificial intelligence could increase productivity by 40 percent or more. Manufacturing, healthcare, retail, and other key industries will benefit. We'll discuss how we're driving operational efficiencies within our organizations with AI applications, from getting started to advanced systems.
By 2035, artificial intelligence could increase productivity by 40 percent or more. Manufacturing, healthcare, retail, and other key industries will benefit. We'll discuss how we're driving operational efficiencies within our organizations with AI applications, from getting started to advanced systems.  Back
 
Topics:
AI and DL Business Track (high level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9941
Streaming:
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Abstract:

This customer panel brings together A.I. implementers who have deployed deep learning at scale using NVIDIA DGX Systems. We'll focus on specific technical challenges we faced, solution design considerations, and best practices learned from implementing our respective solutions. Attendees will gain insights such as: 1) how to set up your deep learning project for success by matching the right hardware and software platform options to your use case and operational needs; 2) how to design your architecture to overcome unnecessary bottlenecks that inhibit scalable training performance; and 3) how to build an end-to-end deep learning workflow that enables productive experimentation, training at scale, and model refinement.

This customer panel brings together A.I. implementers who have deployed deep learning at scale using NVIDIA DGX Systems. We'll focus on specific technical challenges we faced, solution design considerations, and best practices learned from implementing our respective solutions. Attendees will gain insights such as: 1) how to set up your deep learning project for success by matching the right hardware and software platform options to your use case and operational needs; 2) how to design your architecture to overcome unnecessary bottlenecks that inhibit scalable training performance; and 3) how to build an end-to-end deep learning workflow that enables productive experimentation, training at scale, and model refinement.

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Topics:
AI Application Deployment and Inference, AI and DL Business Track (high level), Data Center and Cloud Infrastructure, AI for Business, HPC and Supercomputing
Type:
Panel
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8194
Streaming:
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Abstract:
Deep learning techniques have the potential to enable a step change in modeling efficiency for industrial systems. By increasing efficiency and accuracy of diagnostics, and extracting meaning from large amounts of industrial data, deep learning provides a pathway to truly differentiated outcomes. In this talk, we will discuss our experience building deep learning models for Oil & Gas applications and the CI/CD process for managing the lifecycle of the models in production. We will present novel applications of deep learning for anomaly detection, rock formation identification and optimization. The hybrid modeling framework combining physics-based models with deep learning techniques will be highlighted with specific application of production optimization.
Deep learning techniques have the potential to enable a step change in modeling efficiency for industrial systems. By increasing efficiency and accuracy of diagnostics, and extracting meaning from large amounts of industrial data, deep learning provides a pathway to truly differentiated outcomes. In this talk, we will discuss our experience building deep learning models for Oil & Gas applications and the CI/CD process for managing the lifecycle of the models in production. We will present novel applications of deep learning for anomaly detection, rock formation identification and optimization. The hybrid modeling framework combining physics-based models with deep learning techniques will be highlighted with specific application of production optimization.  Back
 
Topics:
Industrial Inspection
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8789
Streaming:
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