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.
We are working on a set of tutorials for the new imperative interface called Gluon. A preview version is hosted at http://gluon.mxnet.io.
Training and Inference¶
More tutorials and examples are available in the GitHub repository.