Accelerate AI with synthetic data using generative adversarial networks (GAN) (sponsored by NVIDIA)

Description

Synthetic data will drive the next wave of deployment and application of deep learning in the real world across a variety of problems involving speech recognition, image classification, object recognition and language. All industries, and companies will benefit, as synthetic data can create conditions through simulation, instead of authentic situations (virtual worlds enable you to avoid cost of damages, spare human injuries, and other factors that come into play); unparalleled ability to test products, and interactions with them in any environment.

Join us for this introductory session to learn more about how Generative Adversarial Networks (GAN) are successfully used to improve data generation. We will cover specific real world examples where customers have deployed GAN to solve challenges in healthcare, space, transportation, and retail industries.

Renee Yao explains how generative adversarial networks (GAN) are successfully used to improve data generation and explores specific real-world examples where customers have deployed GANs to solve challenges in healthcare, space, transportation, and retail industries.

This session is sponsored by NVIDIA.