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Solving Deep Learning Deployment Challenges with NVIDIA TensorRT

In this webinar we explored some of the common challenges with deep learning deployment and how they can be addressed with NVIDIA TensorRT. Through an example, we will review a typical workflow for taking a trained deep neural network to production to achieve desired throughput, latency and energy efficiency requirements.

Highlights include:
  1. Overview of deep learning deployment and associated challenge
  2. A deep dive into deep learning deployment workflow with TensorRT
  3. Overview of TensorRT optimizer and runtime engine
  4. A live demonstration of deploying a TensorFlow model into an app running TensorRT
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Presented By
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Fausto Milletari
Ph.D. candidate at the Technical University of Munich

Fausto Milletari is a Ph.D. candidate at the Technical University of Munich (TUM) since October 2013. After earning his M.Sc. in informatics, passed with high distinction, he joined the chair for Computer Aided Medical Procedures, directed by Professor Nassir Navab. Fausto's major research topic is segmentation of ultrasound images of the brain. His work focuses on pattern recognition and machine learning, and in particular on voting-based approaches using state-of-the-art learning techniques.
 
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Shashank Prasanna
Product Marketing Manager, NVIDIA

Shashank Prasanna is a product marketing manager at NVIDIA where he focuses on deep learning products and applications. Prior to joining NVIDIA, Shashank worked for MathWorks, makers of MATLAB, focusing on machine learning and data analytics, and for Oracle Corp. designing and developing CRM software. Shashank holds an M.S. in electrical engineering from Arizona State University.
 
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FAUSTO MILLETARI
Senior Solutions Architect, NVIDIA

Fausto Milletari is a Senior Solutions architect at NVIDIA since August 2017. After earning his M.Sc. in informatics, passed with high distinction, he started his Ph.D. at the chair for Computer Aided Medical Procedures a the Technical University of Munich under the supervision of Prof. Nassir Navab. Fausto's major research topic is segmentation of ultrasound images of the brain. His work focuses on pattern recognition and machine learning, and in particular on deep learning techniques to obtain robust segmentation of three-dimensional medical data.
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