A 60-minute Gluon Crash Course

This crash course aims to provide a quick overview of how to use Gluon, the imperative interface of MXNet.

When you finish this Gluon crash course, you will have accomplished the following goals:

  1. Understanding the basic usage of the major components of Gluon
  2. How to train a a basic neural network to predict images


This course assumes that readers have basic knowledge about machine learning and neural networks.

For a high-level introduction to neural networks, you might find Andrew Ng's Coursera class on Neural Networks and Deep Learning useful for acquiring the basic neural networking knowledge.

You can also refer to these excellent hands-on tutorials to learn deep learning from scratch.

Some of the material might stray a little too far into advanced mathematics, but don't be concerned. You can still learn to use neural networks and Gluon without a PhD.


For instructions on running the Jupyter notebooks included with this course, please refer to the README. Otherwise, you can simply read through this course's material.


You may also download the PDF of this material, and packed notebooks in zip or tar.gz format.

Help & Discussions

To get help or ask questions, please use the MXNet forum.


To contribute or report bugs, please refer to the GitHub repo.


  1. The idea to provide a short tutorial like this is from Pytorch.
  2. A large amount of content is adapted from Deep Learning - The Straight Dope.