| #Awesome MXNet |
| |
| This page contains a curated list of awesome MXnet examples, tutorials and blogs. It is inspired by [awesome-php](https://github.com/ziadoz/awesome-php) and [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning). |
| |
| - [Contributing](#contributing) |
| - [List of examples](#list-of-examples) |
| - [Languages Binding Examples](#language-binding-examples) |
| - [Deep Learning Examples](#deep-learning-examples) |
| - [IPython Notebooks](#ipython-notebooks) |
| - [Mobile App Examples](#mobile-apps-examples) |
| - [Web Predictive Services](#web-predictive-services) |
| - [List of tutorials](#list-of-tutorials) |
| - [GPU Technology Conference 2016 Hands-on session](#gtc2016-hands-on) |
| - [Deep learning for hackers with MXnet](#deep-learning-for-hackers) |
| - [MXnet setup on AWS](#mxnet-aws) |
| - [Kaggle tutorials](#kaggle-tutorials) |
| - [Learning Note](#learning-note) |
| - [Machine Learning Challenge Winning Solutions](#winning-solutions) |
| - [Tools with MXnet](#tools-with-mxnet) |
| |
| ## <a name="Contributing"></a>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](https://github.com/dmlc/mxnet/tree/master/example/MXNetTutorialTemplate.ipynb). |
| |
| ##<a name="list-of-examples"></a>List of examples |
| |
| ###<a name="language-binding-examples"></a>Languages Binding Examples |
| ------------------ |
| * [C++ examples](https://github.com/dmlc/mxnet/tree/master/example/image-classification/predict-cpp/) - Example code for using C++ interface, including NDArray, symbolic layer and models. |
| * [MXNet Python](http://mxnet.readthedocs.io/en/latest/api/python/index.html) - Python library |
| * [MXNetR](http://mxnet.readthedocs.io/en/latest/api/r/index.html) - R library |
| * [MXNet.jl](http://mxnetjl.readthedocs.org/en/latest/) - Julia library |
| * [go-mxnet-predictor](https://github.com/songtianyi/go-mxnet-predictor) - Go binding for inference |
| * [gomxnet](https://github.com/jdeng/gomxnet) - Go binding [Outdated] |
| * [MXNet JNI](https://github.com/dmlc/mxnet/tree/master/amalgamation/jni) - JNI(Android) library |
| * [MXNet Amalgamation](https://github.com/dmlc/mxnet/tree/master/amalgamation) - Amalgamation (entire library in a single file) |
| * [MXNet Javascript](https://github.com/dmlc/mxnet.js/) - MXNetJS: Javascript Package for Deep Learning in Browser (without server) |
| |
| ###<a name="deep-learning-examples"></a>Deep Learning Examples |
| -------------- |
| * [Image classification](https://github.com/dmlc/mxnet/tree/master/example/image-classification) - Image classification on MNIST,CIFAR,ImageNet-1k,ImageNet-Full, ***with multiple GPU and distributed training***. |
| * [Recurrent Neural Net](https://github.com/dmlc/mxnet/tree/master/example/rnn) - LSTM and RNN for language modeling and character level generation (Char-RNN). |
| * [Autoencoder](https://github.com/dmlc/mxnet/tree/master/example/autoencoder) - Auto encoder training. |
| * [Numpy Operator Customization](https://github.com/dmlc/mxnet/tree/master/example/numpy-ops) - Example on quick customize new ops with numpy. |
| * [Adversary Sample Generation](adversary) - Find adversary sample by using fast sign method. |
| * [Neural Art](neural-style) - Generate artistic style images. |
| * [DQN and Double DQN](https://github.com/dmlc/mxnet/tree/master/example/reinforcement-learning/dqn) - Examples of training DQN and Double DQN to play Atari Games. |
| * [DDPG](https://github.com/dmlc/mxnet/tree/master/example/reinforcement-learning/ddpg) - Example of training DDPG for CartPole. |
| * [Kaggle 1st national data science bowl](https://github.com/dmlc/mxnet/tree/master/example/kaggle-ndsb1) - a MXnet example for Kaggle Nation Data Science Bowl 1 |
| * [Kaggle 2nd national data science bowl](https://github.com/dmlc/mxnet/tree/master/example/kaggle-ndsb2) - a tutorial for Kaggle Second Nation Data Science Bowl |
| * [CNN for Text Classification](cnn_text_classification) - a MXnet example for text classification |
| * [Chinese plate recognition](https://github.com/imistyrain/mxnet-mr) - Recognize Chinese vehicle plate, by [imistyrain](https://github.com/imistyrain) |
| * [Fast R-CNN](https://github.com/precedenceguo/mx-rcnn) by [Jian Guo](https://github.com/precedenceguo) |
| * "End2End Captcha Recognition (OCR)" by [xlvector](https://github.com/xlvector) [github link](https://github.com/xlvector/learning-dl/tree/master/mxnet/ocr) [Blog in Chinese](http://blog.xlvector.net/2016-05/mxnet-ocr-cnn/) |
| * "Prediction step of xlvector's lstm ocr" by [melody-rain](https://github.com/melody-rain) [github link](https://github.com/melody-rain/mxnet/commit/46002e31fc34c746c01bcaa7ade999187068ad3c) [Blog in Chinese](https://zhuanlan.zhihu.com/p/22698511) |
| * "Solving classificiation + regression with MXnet in Multi Input + Multi Obj" by [xlvector](https://github.com/xlvector) [github link](https://gist.github.com/xlvector/c304d74f9dd6a3b68a3387985482baac) [Blog in Chinese](http://blog.xlvector.net/2016-05/mxnet-regression-classification-for-concret-continuous-features/) |
| * "Learn to sort by LSTM" by [xlvector](https://github.com/xlvector) [github link](https://github.com/xlvector/learning-dl/tree/master/mxnet/lstm_sort) [Blog in Chinese](http://blog.xlvector.net/2016-05/mxnet-lstm-example/) |
| * [Neural Art using extremely lightweight (<500K) neural network](https://github.com/pavelgonchar/neural-art-mini) Lightweight version of mxnet neural art implementation by [Pavel Gonchar](https://github.com/pavelgonchar) |
| * [Neural Art with generative networks](https://github.com/zhaw/neural_style) by [zhaw](https://github.com/zhaw) |
| * [Faster R-CNN in MXNet with distributed implementation and data parallelization](https://github.com/dmlc/mxnet/tree/master/example/rcnn) |
| * [Asynchronous Methods for Deep Reinforcement Learning in MXNet](https://github.com/zmonoid/Asyn-RL-MXNet/blob/master/mx_asyn.py) by [zmonoid](https://github.com/zmonoid) |
| * [Deep Q-learning in MXNet](https://github.com/zmonoid/DQN-MXNet) by [zmonoid](https://github.com/zmonoid) |
| * [Face Detection with End-to-End Integration of a ConvNet and a 3D Model (ECCV16)](https://github.com/tfwu/FaceDetection-ConvNet-3D) by [tfwu](https://github.com/tfwu), source code for paper Yunzhu Li, Benyuan Sun, Tianfu Wu and Yizhou Wang, "Face Detection with End-to-End Integration of a ConvNet and a 3D Model", ECCV 2016 <https://arxiv.org/abs/1606.00850> |
| * [End-to-End Chinese plate recognition base on MXNet](https://github.com/szad670401/end-to-end-for-chinese-plate-recognition) by [szad670401](https://github.com/szad670401) |
| * [Reproduce ResNet-v2 (Identity Mappings in Deep Residual Networks) using MXNet](https://github.com/tornadomeet/ResNet) by [tornadomeet](https://github.com/tornadomeet) |
| * [Learning similarity among images in MXNet](http://www.jianshu.com/p/70a66c8f73d3) by xlvector in Chinese. Github [link](https://github.com/xlvector/learning-dl/tree/master/mxnet/triple-loss) |
| * [Matrix decomposition (SVD) with MXNet](http://www.jianshu.com/p/ebf7bf53ed3e) by xlvector in Chinese. Github [link](https://github.com/xlvector/mxnet/blob/svd/example/svd/svd.py) |
| * [MultiGPU enabled image generative models (GAN and DCGAN)](https://github.com/tqchen/mxnet-gan) by [Tianqi Chen](https://github.com/tqchen) |
| * [Baidu Warp CTC with MXNet](https://github.com/dmlc/mxnet/tree/master/example/warpctc) by xlvector |
| * [Deep reinforcement learning for playing flappybird by mxnet](https://github.com/li-haoran/DRL-FlappyBird) by LIHaoran |
| * [Neural Style in Markov Random Field (MRF) and Perceptual Losses Realtime transfer](https://github.com/zhaw/neural_style) by [zhaw](https://github.com/zhaw) |
| * [MTCNN Face keypoints detection and alignment](https://pangyupo.github.io/2016/10/22/mxnet-mtcnn/) ([github](https://github.com/pangyupo/mxnet_mtcnn_face_detection)) in Chinese by [pangyupo](https://github.com/pangyupo) |
| * [SSD: Single Shot MultiBox Object Detector](https://github.com/zhreshold/mxnet-ssd) by [zhreshold](https://github.com/zhreshold) |
| * [Fast Neural Style in Scala](https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Mxnet-Scala/FastNeuralStyle) by [Ldpe2G](https://github.com/Ldpe2G) |
| * [LSTM Human Activity Recognition](https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Mxnet-Scala/HumanActivityRecognition) by [Ldpe2G](https://github.com/Ldpe2G) |
| * [Visual Question Answering](https://github.com/liuzhi136/Visual-Question-Answering) by [liuzhi136](https://github.com/liuzhi136) |
| |
| ###<a name="ipython-notebooks"></a>IPython Notebooks |
| ----------------- |
| * [Predict with Pre-trained model](https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/predict-with-pretrained-model.ipynb) - Notebook on how to predict with pretrained model. |
| * [composite symbol](https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/composite_symbol.ipynb) - A demo of how to composite a symbolic Inception-BatchNorm Network |
| * [cifar-10 recipe](https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/cifar10-recipe.ipynb) - A step by step demo of how to use MXNet |
| * [cifar-100](https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/cifar-100.ipynb) - A demo of how to train a 75.68% accuracy CIFAR-100 model |
| * [simple bind](https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/simple_bind.ipynb) - A demo of low level training API. |
| * [Multi task tutorial](https://github.com/haria/mxnet-multi-task-example/blob/master/multi-task.ipynb) - A demo of how to train and predict multi-task network on both MNIST and your own dataset. |
| * [class active maps](https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/class_active_maps.ipynb) - A demo of how to localize the discriminative regions in an image using global average pooling (GAP) in CNNs. |
| * [DMLC MXNet Notebooks](https://github.com/dmlc/mxnet-notebooks) DMLC's repo for various notebooks ranging from basic usages of MXNet to state-of-the-art deep learning applications. |
| |
| ###<a name="mobile-apps-examples"></a>Mobile App Examples |
| ------------------- |
| * [MXNet Android Classification App](https://github.com/Leliana/WhatsThis) - Image classification on Android with MXNet. |
| * [MXNet iOS Classification App](https://github.com/pppoe/WhatsThis-iOS) - Image classification on iOS with MXNet. |
| * [Compile MXnet on Xcode (in Chinese)](http://www.liuxiao.org/2015/12/ios-mxnet-%E7%9A%84-ios-%E7%89%88%E6%9C%AC%E7%BC%96%E8%AF%91/) - a step-by-step tutorial of compiling MXnet on Xcode for iOS app |
| |
| ###<a name="web-predictive-services"></a>Web Predictive Services |
| ----------------------- |
| * [MXNet Shinny](https://github.com/thirdwing/mxnet_shiny) - Source code for quickly creating a Shiny R app to host online image classification. |
| * [Machine Eye](http://rupeshs.github.io/machineye/) - Web service for local image file/image URL classification without uploading. |
| |
| ##<a name="list-of-tutorials"></a>List of tutorials |
| |
| ###<a name="gtc2016-hands-on"></a>GPU Technology Conference 2016 Hands-on session |
| |
| * [Video on GTC 2016 site](http://on-demand.gputechconf.com/gtc/2016/video/L6143.html) |
| * [Video backup in Mainland China](http://pan.baidu.com/s/1eS58Gue) |
| * [iPython Notebook](https://github.com/dmlc/mxnet-gtc-tutorial) |
| |
| ###<a name="deep-learning-for-hackers"></a>Deep learning for hackers with MXNet |
| |
| * Deep learning for hackers with MXNet (1) GPU installation and MNIST [English](https://no2147483647.wordpress.com/2015/12/07/deep-learning-for-hackers-with-mxnet-1/) [Chinese](http://phunter.farbox.com/post/mxnet-tutorial1) - 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](https://no2147483647.wordpress.com/2015/12/21/deep-learning-for-hackers-with-mxnet-2/) [Chinese](http://phunter.farbox.com/post/mxnet-tutorial2) - a tutorial of generating Van Gogh style cat paintings. |
| |
| ### <a name="mxnet-aws"></a>MXNet on the cloud |
| * [Setup Amazon AWS GPU instance with MXnet](https://no2147483647.wordpress.com/2016/01/16/setup-amazon-aws-gpu-instance-with-mxnet/) - AWS GPU instance setup with GPU (CUDA with latest cuDNN and S3 support) |
| * [Intro Guide to AWS (MXNet with Julia)](http://www.datasciencebowl.com/aws_guide/) - A step-by-step guide of using spot instances with Amazon Web Services (AWS) to help you save money when training DSB models on MXNet by [Mike Kim](http://www.datasciencebowl.com/author/mikekim/) |
| * [Building Deep Neural Networks in the Cloud with Azure GPU VMs, MXNet and Microsoft R Server](https://blogs.technet.microsoft.com/machinelearning/2016/09/15/building-deep-neural-networks-in-the-cloud-with-azure-gpu-vms-mxnet-and-microsoft-r-server/) by [Cortana Intelligence and ML Blog Team](https://social.technet.microsoft.com/profile/Cortana+Intelligence+and+ML+Blog+Team) at Microsoft |
| * [Applying Deep Learning at Cloud Scale, with Microsoft R Server & Azure Data Lake](https://blogs.technet.microsoft.com/machinelearning/2016/10/31/applying-cloud-deep-learning-at-scale-with-microsoft-r-server-azure-data-lake/) by [Cortana Intelligence and ML Blog Team](https://social.technet.microsoft.com/profile/Cortana+Intelligence+and+ML+Blog+Team) at Microsoft |
| * [Training Deep Neural Neural Networks on ImageNet Using Microsoft R Server and Azure GPU VMs](https://blogs.technet.microsoft.com/machinelearning/2016/11/15/imagenet-deep-neural-network-training-using-microsoft-r-server-and-azure-gpu-vms/) by [Cortana Intelligence and ML Blog Team](https://social.technet.microsoft.com/profile/Cortana+Intelligence+and+ML+Blog+Team) at Microsoft |
| * [Cloud-Scale Text Classification with Convolutional Neural Networks on Microsoft Azure](https://blogs.technet.microsoft.com/machinelearning/2017/02/13/cloud-scale-text-classification-with-convolutional-neural-networks-on-microsoft-azure/) by [Cortana Intelligence and ML Blog Team](https://social.technet.microsoft.com/profile/Cortana+Intelligence+and+ML+Blog+Team) at Microsoft |
| * [Distributed Deep Learning Made Easy](https://aws.amazon.com/blogs/compute/distributed-deep-learning-made-easy/) at AWS/Amazon for deploying deep learning clusters using MXNet |
| |
| ### <a name="kaggle-tutorials"></a>Kaggle tutorials |
| * [Kaggle 2nd Annual Data Science Bowl End-to-End Deep Learning Tutorial (Python)](https://www.kaggle.com/c/second-annual-data-science-bowl/forums/t/18079/end-to-end-deep-learning-tutorial-0-0392) - an end-to-end python tutorial for Kaggle heart disease diagnose competition (public leaderboard score 0.0392) |
| * [Kaggle 2nd Annual Data Science Bowl End-to-End Deep Learning Tutorial (R)](https://www.kaggle.com/c/second-annual-data-science-bowl/forums/t/18122/deep-learning-model-in-r) - an end-to-end R tutorial for Kaggle heart disease diagnose competition |
| * [Dogs vs. Cats classification with mxnet and R](https://statist-bhfz.github.io/cats_dogs_finetune) - end-to-end (not winning) tutorial with an example of fine-tuning in R |
| |
| ### <a name="learning-note"></a>Learning Note |
| * [Learning Note in Chinese](https://github.com/zhubuntu/MXNet-Learning-Note) - MXNet learning note in Chinese. |
| * [Getting Started with MXNet](https://indico.io/blog/getting-started-with-mxnet/) by [indico.io](https://indico.io) (Chinese Translation [MXNet实践](http://www.infoq.com/cn/articles/practise-of-mxnet) by [侠天](http://www.infoq.com/cn/author/%E4%BE%A0%E5%A4%A9) ) |
| * [{mxnet} R package from MXnet, an intuitive Deep Learning framework including CNN & RNN] (http://tjo-en.hatenablog.com/entry/2016/03/30/233848) by [TJO](http://tjo-en.hatenablog.com/) |
| * [MXnet with R: combined power of deep learning](http://cos.name/2016/04/mxnet-r/) in Chinese by Tong He |
| * [Understand MXNet dependency engine](http://yuyang0.github.io/articles/mxnet-engine.html) in Chinese by [Yu Yang](https://github.com/yuyang0) |
| |
| ## <a name="winning-solutions"></a>Machine Learning Challenge Winning Solutions |
| |
| * Dmitrii Tsybulevskii, 1st place of the [Yelp Restaurant Photo Classification](https://www.kaggle.com/c/yelp-restaurant-photo-classification). Link to [the Kaggle interview](http://blog.kaggle.com/2016/04/28/yelp-restaurant-photo-classification-winners-interview-1st-place-dmitrii-tsybulevskii/). |
| |
| ## <a name="tools-with-mxnet"></a>Tools with MXnet |
| * [TensorFuse](https://github.com/dementrock/tensorfuse) - Common interface for Theano, CGT, TensorFlow, and mxnet (experimental) by [dementrock](https://github.com/dementrock) |
| * [MXnet-face](https://github.com/tornadomeet/mxnet-face) - Using mxnet for face-related algorithm by [tornadomeet](https://github.com/tornadomeet) where the single model get 97.13%+-0.88% accuracy on LFW, and with only 20MB size. |
| * [MinPy](https://github.com/dmlc/minpy) - Pure numpy practice with third party operator Integration and MXnet as backend for GPU computing |
