Deep Learning in Apache MxNet Gluon

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 lecture, we’ll provide a short review of deep learning basics, the fundamentals of Gluon, advanced models, and multiple-GPU deployments. Well show how to define neural networks 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.