Tutorial on Deep Learning with Apache MXNet Gluon

  • Alex Smola, PhD, Director of Machine Learning and Deep Learning at AWS
  • When: September 21, 2017 to September 21, 2017, 13:30
  • Where: Bloomberg Center for Physics and Astronomy, Room 462
    Johns Hopkins Homewood Campus
    Baltimore, MD 21218

Unfortunately, registration has reached capacity for the “Tutorial on Deep Learning with Apache MXNet Gluon” by Dr. Alex Smola, Director of Machine Learning and Deep Learning at AWS, Carnegie Mellon University, Marianas Labs, CEO. If you really want to attend, and are curious if any seats have become available, you can email one of our web admins and they will check.

The tutorial will take place on Thursday, September 21st, from 1:30 pm to 5:30 pm in the Bloomberg Building, Room 462, located on the Johns Hopkins Homewood Campus.


Alex Smola, PhD
Alex Smola, PhD

“This tutorial introduces Gluon, a flexible new interface that pairs MXNet’s speed with a user-friendly frontend. Symbolic frameworks like Theano and TensorFlow offer speed and memory efficiency but are harder to program. Imperative frameworks like Chainer and PyTorch are easy to debug but they can seldom compete with the symbolic code when it comes to speed. Gluon reconciles the two, removing a crucial pain point by using just-in-time compilation and an efficient runtime engine for efficiency.

In this crash course, we’ll cover deep learning basics, the fundamentals of Gluon, advanced models, and multiple-GPU deployments. We will walk you through MXNet’s NDArray data structure and automatic differentiation tools. Well show you how to define neural networks at the atomic level, and through Gluon’s predefined layers. We’ll demonstrate how to serialize models and build dynamic graphs. Finally, we will show you how to hybridize your networks, simultaneously enjoying the benefits of imperative and symbolic deep learning.

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