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GTC On-Demand

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Abstract:

Take a journey through the TensorFlow container provided by the NVIDIA GPU Cloud. We'll start with how to launch and navigate inside the container, and stop along the way to explore the included demo scripts, extend the container with extra software, and examine best practices for how to take advantage of all the benefits bundled inside the NGC TensorFlow container. This session will help NGC beginners get the most out of the TensorFlow container and become productive as quickly as possible.

Take a journey through the TensorFlow container provided by the NVIDIA GPU Cloud. We'll start with how to launch and navigate inside the container, and stop along the way to explore the included demo scripts, extend the container with extra software, and examine best practices for how to take advantage of all the benefits bundled inside the NGC TensorFlow container. This session will help NGC beginners get the most out of the TensorFlow container and become productive as quickly as possible.

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Topics:
AI Application Deployment and Inference, Deep Learning and AI Frameworks
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9256
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Abstract:

Survey of successful deep learning (DL) applications within several domains featuring continuous streaming data [ time-series ]. Overview of what network architectures have yielded results and why these networks work. Network architectures reviewed included: RNNs (dynamic models and prediction), CNNs (for frequency transformed time series data, i.e., spectrograms), Autoencoders (anomaly detection and unsupervised data-structure visualization), and deep MLPs (sliding window event detection and classification). Example case studies: Industrial { Industrial Robotics, Automotive Telematics, Prognostics/Zero-Down-Time }, IoT { Event & Anomaly Detection, Information Leakage Attacks/Defenses }, Financial { Limit Books, Mortgage Risk Markets}.

Survey of successful deep learning (DL) applications within several domains featuring continuous streaming data [ time-series ]. Overview of what network architectures have yielded results and why these networks work. Network architectures reviewed included: RNNs (dynamic models and prediction), CNNs (for frequency transformed time series data, i.e., spectrograms), Autoencoders (anomaly detection and unsupervised data-structure visualization), and deep MLPs (sliding window event detection and classification). Example case studies: Industrial { Industrial Robotics, Automotive Telematics, Prognostics/Zero-Down-Time }, IoT { Event & Anomaly Detection, Information Leakage Attacks/Defenses }, Financial { Limit Books, Mortgage Risk Markets}.

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Topics:
Intelligent Machines and IoT, Deep Learning and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7378
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