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GTC ON-DEMAND

Intelligent Machines, IoT & Robotics
Presentation
Media
Deep Learning Approaches to Timeseries Data
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}.

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