MXNet tutorials can be found in this section. A variety of language bindings are available for MXNet (including Python, Scala, Java, Clojure, C++ and R) and we have a different tutorial section for each language.

Are you new to MXNet, and don’t have a preference on language? We currently recommend starting with Python, and specifically the Gluon APIs (versus Module APIs) as they’re more flexible and easier to debug.

Another great resource for learning MXNet is our examples section which includes a wide variety of models (from basic to state-of-the-art) for a wide variety of tasks including: object detection, style transfer, reinforcement learning, and many others.


Python Tutorials

We have two types of API available for Python: Gluon APIs and Module APIs. See here for a comparison.

A comprehensive introduction to Gluon can be found at Dive into Deep Learning. Structured like a book, it build up from first principles of deep learning and take a theoretical walkthrough of progressively more complex models using the Gluon API. Also check out the 60-Minute Gluon Crash Course if you’re short on time or have used other deep learning frameworks before.

Use the tutorial selector below to filter to the relevant tutorials. You might see a download link in the top right corner of some tutorials. Use this to download a Jupyter Notebook version of the tutorial, and re-run and adjust the code as you wish.

Select API:

Contributing Tutorials

We really appreciate contributions, and tutorials are a great way to share your knowledge and help the community. After you have followed these steps, please submit a pull request on Github.

And if you have any feedback on this section please raise an issue on Github.