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.
- Contributing
- List of examples
- List of tutorials
- Machine Learning Challenge Winning Solutions
- Tools with MXnet
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.
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/index.md for your tutorial to show up on the website.
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):
<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
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 . Like this:
<!--notebook-skip-line-->
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.
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/test_tutorials.py. (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/ubuntu_tutorials.sh
- MXNet C++ API
- C++ examples - Example code for using C++ interface, including NDArray, symbolic layer and models.
- MXNet Python API
- MXNet Java API
- MXNet Scala API
- MXNet R API
- MXNet Julia API
- MXNet Perl API
- go-mxnet-predictor - Go binding for inference
- MXNet JNI - JNI(Android) library
- MXNet Amalgamation - Amalgamation (entire library in a single file)
- MXNet Javascript - MXNetJS: Javascript Package for Deep Learning in Browser (without server)
- Autoencoder - unsupervised feature learning
- Bayesian Methods - various examples related to Bayesian Methods
- Bidirectional LSTM Sorting - use a bidirectional LSTM to sort an array
- Caffe - how to call Caffe operators from MXNet
- CNN for Chinese Text Classification - a MXnet example for Chinese text classification
- CNN for Text Classification - a MXnet example for text classification
- CTC with MXNet - a modification of warpctc
- Deep Embedded Clustering - unsupervised deep embedding for clustering analysis
- Dense-Sparse-Dense Training - Dense-Sparse-Dense Training for deep neural networks
- Fully Convolutional Networks - fully convolutional networks for semantic segmentation
- Generative Adversarial Networks with R - GAN examples in R
- Gluon Examples - several examples using the Gluon API
- Style Transfer - a style transfer example using gluon
- Word Language Model - an example that trains a multi-layer RNN on the Penn Treebank language modeling benchmark
- SN-GAN - an example that utilizes spectral normalization to train GAN(Generative adversarial network) using Gluon API
- Image Classification with R - image classification on MNIST,CIFAR,ImageNet-1k,ImageNet-Full, with multiple GPU and distributed training.
- Kaggle 1st national data science bowl - a MXnet example for Kaggle Nation Data Science Bowl 1
- Kaggle 2nd national data science bowl - a tutorial for Kaggle Second Nation Data Science Bowl
- Memory Cost - a script to show the memory cost of different allocation strategies
- Model Parallelism - various model parallelism examples
- Model Parallelism with LSTM - an example showing how to do model parallelism with a LSTM
- Model Parallelism with Matrix Factorization - a matrix factorization algorithm for recommendations
- Module API - examples with the Python Module API
- Multi-task Learning - how to use MXNet for multi-task learning
- MXNet Adversarial Variational Autoencoder - combines a variational autoencoder with a generative adversarial network
- Noise-contrastive estimation loss - used to speedup multi-class classification
- Neural Style - use deep learning for style transfer in images
- Numpy Operator Customization - Examplea on quick customize new ops with Numpy
- Profiling - generate profiling results in json files
- Quantization and Calibration Examples - examples of quantizing a FP32 model to INT8 and performing low-precision inference with Intel MKL-DNN on CPU or cuDNN on GPU
- R-CNN - R-CNN with distributed implementation and data parallelization
- Recommender Systems - examples of how to build various kinds of recommender systems
- Reinforcement Learning - a variety of reinforcement learning examples
- A3C
- DDPG - example of training DDPG for CartPole
- DQN - examples of training DQN and Double DQN to play Atari Games
- Parallel Advantage-Actor Critic
- Restricted Boltzmann Machine - an example of the binary restricted Boltzmann machine learning MNIST
- RNN Time Major - RNN implementation with Time-major layout
- Recurrent Neural Net - creating recurrent neural networks models using high level
mxnet.rnninterface - Sparse - a variety of sparse examples
- Single Shot MultiBox Detector - SSD object recognition example
- Stochastic Depth - implementation of the stochastic depth algorithm
- Support Vector Machine - an SVM example using MNIST
- Variational Auto Encoder - implements the Variational Auto Encoder in MXNet using MNIST
- Face Recognition with ArcFace - ONNX model for face recognition with notebooks for training, validating and running inference in MXNet by abhinavs95
- Chinese plate recognition - Recognize Chinese vehicle plate, by imistyrain
- Fast R-CNN by Jian Guo
- "End2End Captcha Recognition (OCR)" by xlvector github link Blog in Chinese
- "Prediction step of xlvector's lstm ocr" by melody-rain github link Blog in Chinese
- "Solving classification + regression with MXnet in Multi Input + Multi Obj" by xlvector github link Blog in Chinese
- "Learn to sort by LSTM" by xlvector github link Blog in Chinese
- Neural Art using extremely lightweight (<500K) neural network Lightweight version of mxnet neural art implementation by Pavel Gonchar
- Neural Art with generative networks by zhaw
- Faster R-CNN in MXNet with distributed implementation and data parallelization
- Asynchronous Methods for Deep Reinforcement Learning in MXNet by zmonoid
- Deep Q-learning in MXNet by zmonoid
- Face Detection with End-to-End Integration of a ConvNet and a 3D Model (ECCV16) by 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 by szad670401
- Reproduce ResNet-v2 (Identity Mappings in Deep Residual Networks) using MXNet by tornadomeet
- Learning similarity among images in MXNet by xlvector in Chinese. Github link
- Matrix decomposition (SVD) with MXNet by xlvector in Chinese. Github link
- MultiGPU enabled image generative models (GAN and DCGAN) by Tianqi Chen
- Deep reinforcement learning for playing flappybird by mxnet by LIHaoran
- Neural Style in Markov Random Field (MRF) and Perceptual Losses Realtime transfer by zhaw
- MTCNN Face keypoints detection and alignment (github) in Chinese by pangyupo
- SSD: Single Shot MultiBox Object Detector by zhreshold
- Fast Neural Style in Scala by Ldpe2G
- LSTM Human Activity Recognition by Ldpe2G
- Visual Question Answering by liuzhi136
- Deformable ConvNets (github) by MSRACVer
- OCR with bi-LSTM and CTC Loss in Gluon by ThomasDelteil
- Visual Search with Gluon and HNSWlib, by ThomasDelteil, online demo here
- MXNet-face - Using MXNet for a face-related algorithm by tornadomeet where the single model gets 97.13%+-0.88% accuracy on LFW, and with only 20MB size
- 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.
- AWS Seoul Summit 2017 Demos The demo codes and ipython notebooks in AWS Seoul Summit 2017.
- Character-level CNN for text classification Performing category classification on Amazon reviews using Gluon and character-level Convolutional Neural Networks. Online demo here
- MXNet Android Classification App - Image classification on Android with MXNet.
- MXNet iOS Classification App - Image classification on iOS with MXNet.
- Compile MXnet on Xcode (in Chinese) - a step-by-step tutorial of compiling MXnet on Xcode for iOS app
- 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.
- 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.
- 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) - 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
- Building Deep Neural Networks in the Cloud with Azure GPU VMs, MXNet and Microsoft R Server by Cortana Intelligence and ML Blog Team at Microsoft
- Applying Deep Learning at Cloud Scale, with Microsoft R Server & Azure Data Lake by Cortana Intelligence and ML Blog Team at Microsoft
- Training Deep Neural Neural Networks on ImageNet Using Microsoft R Server and Azure GPU VMs by Cortana Intelligence and ML Blog Team at Microsoft
- Cloud-Scale Text Classification with Convolutional Neural Networks on Microsoft Azure by Cortana Intelligence and ML Blog Team at Microsoft
- Distributed Deep Learning Made Easy at AWS/Amazon for deploying deep learning clusters using MXNet
- Kaggle 2nd Annual Data Science Bowl End-to-End Deep Learning Tutorial (Python) - 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) - an end-to-end R tutorial for Kaggle heart disease diagnose competition
- Dogs vs. Cats classification with mxnet and R - end-to-end (not winning) tutorial with an example of fine-tuning in R
- Learning Note in Chinese - MXNet learning note in Chinese.
- Getting Started with MXNet by indico.io (Chinese Translation MXNet实践 by 侠天 )
- {mxnet} R package from MXnet, an intuitive Deep Learning framework including CNN & RNN by TJO
- MXnet with R: combined power of deep learning in Chinese by Tong He
- Understand MXNet dependency engine in Chinese by Yu Yang
- Dmitrii Tsybulevskii, 1st place of the Yelp Restaurant Photo Classification. Link to the Kaggle interview.
- 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