NVIDIA WEBINAR
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Date: Thursday, September 17, 2020
Time: 10:00am – 11:00am PT
Duration: 1 hour
Time: 10:00am – 11:00am PT
Duration: 1 hour
In this webinar, Eric VanBuhler, Software Engineer at alwaysAI, will walk you through how to use tools and pre-trained models from alwaysAI to quickly develop and deploy computer vision applications in Python on the NVIDIA Jetson™ platform. Watch a demo running an object detection and semantic segmentation algorithms on the Jetson Nano, Jetson TX2, and Jetson Xavier NX.
Find out how to develop AI-based computer vision applications using alwaysAI with minimal coding, and deploy on the NVIDIA Jetson for real-time performance in applications for retail, robotics, smart cities, manufacturing, and more. AlwaysAI tools make it easy for developers with no experience in AI to quickly develop and scale their application.
In this webinar you’ll learn how to:
- Use alwaysAI development tools and the pre-trained models on the Jetson platform/li>
- Run a real-time computer vision application on the NVIDIA Jetson Nano, Jetson TX2, and Jetson Xavier NX with an in-depth demo
WEBINAR REGISTRATION
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DGX Station Whitepaper
Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
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.
Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
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Speaker
Eric VanBuhler
Software Engineer, alwaysAIEric joined the alwaysAI engineering team in March 2019 after 8 years at Qualcomm, and works primarily on the edgeIQ Python library and runtime environment. Eric received his BS in Electrical Engineering from University of Michigan and his MS in Electrical Engineering from University of California, San Diego. He has co-authored a book and holds a patent, both in the field of distributed network optimization.
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