MXNet has two primary high-level interfaces for its deep learning engine: the Gluon API and the Module API. Tutorials for each are provided below.

The difference between the two is an imperative versus symbolic programming style. Gluon makes it easy to prototype, build, and train deep learning models without sacrificing training speed by enabling both (1) intuitive imperative Python code development and (2) faster execution by automatically generating a symbolic execution graph using the hybridization feature.

TL;DR: If you are new to deep learning or MXNet, you should start with the Gluon tutorials.

The Gluon and Module tutorials are in Python, but you can also find a variety of other MXNet tutorials, such as R, Scala, and C++ in the Other Languages API Tutorials section below.

Example scripts and applications as well as contribution info is below.

Example Scripts and Applications

More tutorials and examples are available in the GitHub repository.

Learn More About Gluon!

Most of the Gluon tutorials are hosted on gluon.mxnet.io, and you may want to follow the chapters on directly the Gluon site.

Contributing Tutorials

Want to contribute an MXNet tutorial? To get started, review these details on example and tutorial writing.