Gluon is the high-level interface for MXNet. It is more intuitive and easier to use than the lower level interface. Gluon supports dynamic (define-by-run) graphs with JIT-compilation to achieve both flexibility and efficiency.
This is a selected subset of Gluon tutorials that explains basic usage of Gluon and fundamental concepts in deep learning. For the comprehensive tutorial on Gluon that covers topics from basic statistics and probability theory to reinforcement learning and recommender systems, please see gluon.mxnet.io.
These tutorials introduce a few fundamental concepts in deep learning and how to implement them in MXNet. The Basics section contains tutorials on manipulating arrays, building networks, loading/preprocessing data, etc. The Training and Inference section talks about implementing Linear Regression, training a Handwritten digit classifier using MLP and CNN, running inferences using a pre-trained model, and lastly, efficiently training a large scale image classifier.
Training and Inference¶
More tutorials and examples are available in the GitHub repository.