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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.

List of examples

Languages Binding Examples


Deep Learning Examples


IPython Notebooks


  • Predict with Pre-trained model - Notebook on how to predict with pretrained model.
  • composite symbol - A demo of how to composite a symbolic Inception-BatchNorm Network
  • cifar-10 recipe - A step by step demo of how to use MXNet
  • cifar-100 - A demo of how to train a 75.68% accuracy CIFAR-100 model
  • simple bind - A demo of low level training API.
  • Multi task tutorial - A demo of how to train and predict multi-task network on both MNIST and your own dataset.
  • class active maps - A demo of how to localize the discriminative regions in an image using global average pooling (GAP) in CNNs.
  • DMLC MXNet Notebooks DMLC's repo for various notebooks ranging from basic usages of MXNet to state-of-the-art deep learning applications.

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