Agenda:6:00-7:00: Socializing (Thanks GridGain for food & drinks!)7:00-7:10: Announcements7:10-8:10: Deep Learning for Engineers: Using Java to deploy Deep Learning models8:10-8:30: Q&A
Abstract:AI is evolving rapidly, & much of the recent advancement is driven by Deep Learning, a machine learning technique inspired by the inner-working of the human brain. In this session, we will discuss what deep learning is, & the new capabilities it enables. We will dive into a few computer vision models that are demonstrating super-human performance, & to integrate these models into your existing Java system leveraging Apache MXNet - an open source deep learning framework & MXNets Java API. By the end of the session, you will learn how to leverage deep learning models in your Java-based systems, the various gotchas involved, & where/how you learn more.
Andrew AyresAndrew is a SDE in Amazon Deep Engine & one of the authors of MXNet Java API. Previous work includes cryptography for the Key Management Service on AWS, machine learning for IBM Watson, & performing research at Oak Ridge National Laboratory. He graduated with a Ph.D. in Nuclear Physics from the University of Tennessee in 2014. While there his focus was on stellar nuclear reactions & supernova simulations.
Qing LanQing is a SDE in Amazon Deep Engine & one of the authors of MXNet Java API. He graduated with a M.S. in Computer Engineering from Columbia University in 2017. He is experienced in Deep Learning, Programming Language Translator & distributed systems. Qing is also a Committer of Apache MXNet.