tree: b731058eefb9ef3849e2a06c20bc835416961b1b [path history] [tgz]
  1. MXNetTutorialTemplate.ipynb
  3. adversary/
  4. autoencoder/
  5. bayesian-methods/
  6. bi-lstm-sort/
  7. caffe/
  8. capsnet/
  9. captcha/
  10. cnn_chinese_text_classification/
  11. cnn_text_classification/
  12. cnn_visualization/
  13. ctc/
  14. deep-embedded-clustering/
  15. distributed_training/
  16. dsd/
  17. fcn-xs/
  18. gan/
  19. gluon/
  20. image-classification/
  21. kaggle-ndsb1/
  22. kaggle-ndsb2/
  23. memcost/
  24. model-parallel/
  25. module/
  26. multi-task/
  27. multivariate_time_series/
  28. mxnet_adversarial_vae/
  29. named_entity_recognition/
  30. nce-loss/
  31. neural-style/
  32. notebooks/
  33. numpy-ops/
  34. onnx/
  35. profiler/
  36. python-howto/
  37. quantization/
  38. rcnn/
  39. recommenders/
  40. reinforcement-learning/
  41. restricted-boltzmann-machine/
  42. rnn-time-major/
  43. rnn/
  44. sparse/
  45. speech_recognition/
  46. ssd/
  47. stochastic-depth/
  48. svm_mnist/
  49. svrg_module/
  50. utils/
  51. vae/

MXNet Examples

This page contains a curated list of awesome MXNet examples, tutorials and blogs. It is inspired by awesome-php and awesome-machine-learning. See also Awesome-MXNet for a similar list.


If you want to contribute to this list and the examples, please open a new pull request.


Example applications or scripts should be submitted in this example folder.


If you have a tutorial idea for the website, download the Jupyter notebook tutorial template.

Tutorial location

Notebook tutorials should be submitted in the docs/tutorials folder, so that they maybe rendered in the web site's tutorial section.

Do not forget to udpdate the docs/tutorials/ for your tutorial to show up on the website.

Tutorial formatting

The site expects the format to be markdown, so export your notebook as a .md via the Jupyter web interface menu (File > Download As > Markdown). Then, to enable the download notebook button in the web site's UI (example), add the following as the last line of the file (example):


If you want some lines to show-up in the markdown but not in the generated notebooks, add this comment <!--notebook-skip-line--> after your ![png](img_url). Like this:


Typically when you have a plt.imshow() you want the image tag [png](img.png) in the .md but not in the downloaded notebook as the user will re-generate the plot at run-time.

Tutorial tests

As part of making sure all our tutorials are running correctly with the latest version of MXNet, each tutorial is run automatically through a python2 and python3 jupyter notebook kernel in the CI, in a GPU environment, checking for errors and warnings.

Add your own test here tests/tutorials/ (If you forget, don't worry your PR will not pass the sanity check).

If your tutorial depends on specific packages, simply add them to this provisionning script: ci/docker/install/

List of examples

Languages Binding Examples

Deep Learning Examples in the MXNet Project Repository

Other Deep Learning Examples with MXNet

IPython Notebooks

Mobile App Examples

Web Predictive Services

  • MXNet Shinny - Source code for quickly creating a Shiny R app to host online image classification.
  • Machine Eye - Web service for local image file/image URL classification without uploading.

List of tutorials

GPU Technology Conference 2016 Hands-on session

Deep learning for hackers with MXNet

  • Deep learning for hackers with MXNet (1) GPU installation and MNIST English Chinese - a tutorial of installing MXnet with GPU and introduction to deep learning by MNIST example.
  • Deep learning for hackers with MXNet (2): Neural art English Chinese - a tutorial of generating Van Gogh style cat paintings.

MXNet on the cloud

Kaggle tutorials

Learning Note

Machine Learning Challenge Winning Solutions

Tools with MXnet

  • TensorFuse - Common interface for Theano, CGT, TensorFlow, and mxnet (experimental) by dementrock
  • MXnet-face - Using mxnet for face-related algorithm by tornadomeet where the single model get 97.13%+-0.88% accuracy on LFW, and with only 20MB size.
  • MinPy - Pure numpy practice with third party operator Integration and MXnet as backend for GPU computing
  • MXNet Model Server - a flexible and easy to use tool for serving Deep Learning models