Tutorial on Deep Learning with Apache MXNet Gluon

Abstract:

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.

Pre-setup:
1) Preferably, have Python3 installed (but Python2.7 will still work).

2) Install mxnet, either building from source or using
pip install mxnet —-pre
Detailed instructions: https://mxnet.incubator.apache.org/get_started/install.html

3) Install Jupyter
http://jupyter.readthedocs.io/en/latest/install.html

4) Clone a copy of the tutorials:
https://github.com/zackchase/mxnet-the-straight-dope

About the Speaker:

Alex Smola is a well-known figure in machine learning, and recently joined Amazon AWS as their director of Machine Learning and Deep Learning.