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Introduction

Date: Wednesday, May 27, 2020
Time: 10:00am – 11:00am CET
Duration: 1 hour


This webinar teaches you how to build object detection and tracking models to analyze data from large-scale video streams using NVIDIA DeepStream technology. You will learn how to build, train, and deploy deep learning models to analyze large volumes of video data. 5G networking is central to intelligent connectivity as it allows for low latency transfers of huge volumes of video and other IOT data. The knowledge gathered in this course can be applied to a wide range of 5G centered intelligent video analytics applications.


By attending this webinar, you'll learn:
  • During the webinar we will use the data gathered from NVIDIA parking lot camera feeds
  • Guide you through steps required to build a hardware-accelerated traffic management system
Join us after the presentation for a live Q&A session.

WEBINAR REGISTRATION

THANK YOU FOR REGISTERING FOR THE WEBINAR



You will receive an email with instructions on how to join the webinar shortly.

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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

ADAM GRZYWACZEWSKI

Senior Deep Learning Data Scientist, NVIDIA

Adam Grzywaczewski is a senior deep learning data scientist at NVIDIA, where his primary responsibility is to support a wide range of customers in delivery of their deep learning solutions. Adam is an applied research scientist specializing in machine learning with a background in deep learning and system architecture. Previously, he was responsible for building up the UK government’s machine-learning capabilities while at Capgemini and worked in the Jaguar Land Rover Research Centre, where he was responsible for a variety of internal and external projects and contributed to the self-learning car portfolio.

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Register

Webinar: Description here

Date & Time: Wednesday, April 22, 2018