Crash Course on Apache MXNet Gluon

About the Event:

This tutorial introduces Gluon, a flexible new interface that pairs MXNet’s speed with a user-friendly front end. In the past, deep learning practitioners had to choose between ease of use and speed when choosing frameworks. On one side, there were symbolic frameworks like Theano and TensorFlow. These offer speed and memory efficiency but are harder to program, can be a pain to debug, and don’t allow for many native language features such as basic control flow. On the other side, there are imperative frameworks like Chainer and PyTorch. They’re a joy to program and 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. Gluon can run as a fully imperative framework. In this mode, you enjoy native language features, painless debugging, and rapid prototyping. You can also effortlessly deploy arbitrarily complex models with dynamic graphs. But when you need more performance, Gluon can also provide the speed of MXNet’s symbolic API by calling down to Gluon’s just-in-time compiler. In this crash course, we will 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. We will show you how to define neural networks at the atomic level, and through Gluon’s predefined layers. We will 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.

About the Speaker:

Zachary Lipton is a mad scientist at Amazon AI and assistant professor at Carnegie Mellon University (2018-). He researches ML methods, applications (healthcare, NLP, RecSys), and social impacts. In addition to corralling deep neural neurons and starting fires on Twitter (@zacharylipton), he is the editor of the Approximately Correct blog and lead author of Deep Learning – The Straight Dope, an interactive book teaching deep learning and MXNet Gluon through Jupyter notebooks.

Language: English