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.
Note: We are working on a set of tutorials for the new imperative interface called Gluon. A preview version is hosted at https://gluon.mxnet.io.
Training and Inference¶
- Callback Function
- Char RNN Example
- Classify Images with a PreTrained Model
- Custom Iterator Tutorial
- Custom Loss Function
- Develop a Neural Network with MXNet in Five Minutes
- Handwritten Digits Classification Competition
- NDArray: Vectorized Tensor Computations on CPUs and GPUs
- Symbol and Automatic Differentiation
More tutorials and examples are available in the GitHub repository.