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  1. adversary/
  2. autoencoder/
  3. bayesian-methods/
  4. bi-lstm-sort/
  5. caffe/
  6. captcha/
  7. cnn_text_classification/
  8. dec/
  9. fcn-xs/
  10. gan/
  11. image-classification/
  12. kaggle-ndsb1/
  13. kaggle-ndsb2/
  14. memcost/
  15. model-parallel-lstm/
  16. module/
  17. multi-task/
  18. nce-loss/
  19. neural-style/
  20. notebooks/
  21. numpy-ops/
  22. profiler/
  23. python-howto/
  24. rcnn/
  25. recommenders/
  26. reinforcement-learning/
  27. rnn/
  28. rnn-time-major/
  29. speech-demo/
  30. speech_recognition/
  31. ssd/
  32. stochastic-depth/
  33. svm_mnist/
  34. torch/
  35. utils/
  36. warpctc/
  37. MXNetTutorialTemplate.ipynb
  38. README.md
example/README.md

Awesome MXNet

This page contains a curated list of awesome MXnet examples, tutorials and blogs. It is inspired by awesome-php and awesome-machine-learning.

Contributing

If you want to contribute to this list and the examples, please open a new pull request. To get started, download the tutorial template.

List of examples

Languages Binding Examples


Deep Learning Examples


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