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Join us and learn how to train a deep neural network (DNN) model that's ready to perform real-time object detection on GPUs.

In this webinar, Gary Burnett, NVIDIA Solution Architect, will demonstrate an end-to-end deep learning inference pipeline on NVIDIA GPUs. He’ll start from a trained object-detection model, optimize it using NVIDIA TensorRT™, and deploy it to perform real-time inference using INT8 precision. TensorRT can deliver up to 8X faster throughput through layer and tensor fusions, as well as INT8 precision on Tensor Cores.

By attending this webinar, you'll learn:
  • How to accelerate an end-to-end inference pipeline on GPUs
  • How to use INT8 precision to get high throughput on GPUs
  • Resources, code, tools, tips, and tricks to get the most out of your deep learning applications

ONDEMAND WEBINAR REGISTRATION

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Be sure to watch the first webinar in this series, Putting Deep Learning to Work.

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DGX Station Datasheet

Get a quick low-down and technical specs for the DGX Station.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.

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Speaker

Gary Burnett

Solution Architect, NVIDIA

Gary is a Solution Architect at NVIDIA on the Professional Visualizations team working in Media and Entertainment. He joined NVIDIA in 2017 after graduating from MIT with degrees in computer science and neuroscience. Gary’s role involves working directly with customers in order to create applications that leverage deep learning for visual effects including image processing, character locomotion, and pose estimation.

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Date & Time: Wednesday, April 22, 2018