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Introduction

Date: Thursday, June 4, 2020
Time: 10:00am – 11:00am PT
Duration: 1 hour


Learn how to build speech recognition, natural language understanding, and speech synthesis services with NVIDIA NeMo and Jarvis. First, we'll cover the basics of the NeMo toolkit for training and fine-tuning conversational AI models on your data. Then, we'll discuss how to use Jarvis to deploy and combine these services into a complete conversational AI solution.


By attending this webinar, you'll learn how to:
  • Build conversational AI models using neural modules
  • Apply transfer learning to fine-tune these models for new domains
  • Deploy trained models to a production service
Join us after the presentation for a live Q&A session.

WEBINAR REGISTRATION

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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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Speakers

Oleksii Kuchaiev

Senior Applied Scientist, NVIDIA

Oleksii is an applied scientist at NVIDIA working on deep-learning algorithms and tools for speech and NLP. Before NVIDIA, he was at Apple, where he worked on AppStore recommendations. And before Apple, he was at Microsoft, where he helped to create the first version of Microsoft's CNTK. Oleksii has a Ph.D. in computer science from the University of California, Irvine.

Ryan Leary

Senior Applied Research Scientist, NVIDIA

Ryan is a senior applied research scientist at NVIDIA focusing on conversational AI research and production platforms. Prior to joining NVIDIA, Ryan served as the CTO of Qntfy, an NLP for Healthcare startup, where he was responsible for setting the technical direction of the company and leading the deep learning research team. Before Qntfy, Ryan worked as a Research Scientist at BBN Technologies in the Speech, Language and Multimedia department focusing on algorithms for large-scale automatic speech recognition deployments and speaker identification. He has published research in peer-reviewed venues on machine learning techniques tailored for scalability and performance, natural language processing, and speech recognition. Ryan holds an M.S. in Electrical & Computer Engineering from Johns Hopkins University, and a B.S. in Computer Science from Rensselaer Polytechnic Institute.

Patrice Castonguay

Senior Deep Learning Applied Scientist, NVIDIA

Patrice Castonguay is a senior deep learning applied scientist at NVIDIA. With a background in mathematics, physics, and high-performance computing, his work at NVIDIA focuses on developing GPU-accelerated conversational AI software. He holds a Ph.D. in aerospace and aeronautics and a minor in computational and mathematical engineering from Stanford University in California.

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Webinar: Description here

Date & Time: Wednesday, April 22, 2018