piiswrong closed pull request #9356: added updated examples to list and contrib info URL: https://github.com/apache/incubator-mxnet/pull/9356
This is a PR merged from a forked repository. As GitHub hides the original diff on merge, it is displayed below for the sake of provenance: As this is a foreign pull request (from a fork), the diff is supplied below (as it won't show otherwise due to GitHub magic): diff --git a/example/README.md b/example/README.md index 507b144ad6..d6bb464183 100644 --- a/example/README.md +++ b/example/README.md @@ -1,6 +1,6 @@ -# Awesome MXNet +# MXNet Examples -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). +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) @@ -20,35 +20,92 @@ This page contains a curated list of awesome MXnet examples, tutorials and blogs ## <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). +If you want to contribute to this list and the examples, please open a new pull request. + +### Examples + +Example applications or scripts should be submitted in this `example` folder. + +### Tutorials + +If you have a tutorial idea for the website, download the [ Jupyter notebook tutorial template](https://github.com/dmlc/mxnet/tree/master/example/MXNetTutorialTemplate.ipynb). + +Notebook tutorials should be submitted in the `docs/tutorials` folder, so that they maybe rendered in the [web site's tutorial section](https://mxnet.incubator.apache.org/tutorials/index.html). + +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](https://mxnet.incubator.apache.org/tutorials/python/linear-regression.html)), add the following as the last line of the file ([example](https://github.com/apache/incubator-mxnet/blame/master/docs/tutorials/python/linear-regression.md#L194)): + +``` +<!-- INSERT SOURCE DOWNLOAD BUTTONS --> +``` ## <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 +* [MXNet C++ API](http://mxnet.incubator.apache.org/api/c++/index.html) + - [C++ examples](https://github.com/apache/incubator-mxnet/tree/master/example/image-classification/predict-cpp) - Example code for using C++ interface, including NDArray, symbolic layer and models. +* [MXNet Python API](http://mxnet.incubator.apache.org/api/python/index.html) +* [MXNet Scala API](http://mxnet.incubator.apache.org/api/scala/index.html) +* [MXNet R API](http://mxnet.incubator.apache.org/api/r/index.html) +* [MXNet Julia API](http://mxnet.incubator.apache.org/api/julia/index.html) +* [MXNet Perl API](https://mxnet.incubator.apache.org/api/perl/index.html) * [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 +### <a name="deep-learning-examples"></a>Deep Learning Examples in the MXNet Project Repository -------------- -* [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 +* [Autoencoder](autoencoder) - unsupervised feature learning +* [Bayesian Methods](bayesian-methods) - various examples related to Bayesian Methods +* [Bidirectional LSTM Sorting](bi-lstm-sort) - use a bidirectional LSTM to sort an array +* [Caffe](caffe) - how to call Caffe operators from MXNet +* [CNN for Chinese Text Classification](cnn_chinese_text_classification) - a MXnet example for Chinese text classification * [CNN for Text Classification](cnn_text_classification) - a MXnet example for text classification +* [CTC with MXNet](ctc) - a modification of warpctc +* [Deep Embedded Clustering](deep-embedded-clustering) - unsupervised deep embedding for clustering analysis +* [Dense-Sparse-Dense Training](dsd) - Dense-Sparse-Dense Training for deep neural networks +* [Fully Convolutional Networks](fcn) - fully convolutional networks for semantic segmentation +* [Generative Adversarial Networks with R](gan/CGAN_mnist_R) - GAN examples in R +* [Gluon Examples](gluon) - several examples using the Gluon API + * [Style Transfer](gluon/style_transfer) - a style transfer example using gluon + * [Word Language Model](gluon/word_language_model) - an example that trains a multi-layer RNN on the Penn Treebank language modeling benchmark +* [Image Classification with R](image-classification) - image classification on MNIST,CIFAR,ImageNet-1k,ImageNet-Full, with multiple GPU and distributed training. +* [Kaggle 1st national data science bowl](kaggle-ndsb1) - a MXnet example for Kaggle Nation Data Science Bowl 1 +* [Kaggle 2nd national data science bowl](kaggle-ndsb2) - a tutorial for Kaggle Second Nation Data Science Bowl +* [Memory Cost](memcost) - a script to show the memory cost of different allocation strategies +* [Model Parallelism](model-parallel) - various model parallelism examples + * [Model Parallelism with LSTM](model-parallel/lstm) - an example showing how to do model parallelism with a LSTM + * [Model Parallelism with Matrix Factorization](model-parallel/lstm) - a matrix factorization algorithm for recommendations +* [Module API](module) - examples with the Python Module API +* [Multi-task Learning] - how to use MXNet for multi-task learning +* [MXNet Adversarial Variational Autoencoder](mxnet_adversarial_vae) - combines a variational autoencoder with a generative adversarial network +* [Noise-contrastive estimation loss](nce-loss) - used to speedup multi-class classification +* [Neural Style](neural-style) - use deep learning for style transfer in images +* [Numpy Operator Customization](numpy-ops) - Examplea on quick customize new ops with Numpy +* [Profiling](profiler) - generate profiling results in json files +* [Python How To](python-howto) - a variety of Python examples +* [R-CNN](rcnn) - R-CNN with distributed implementation and data parallelization +* [Recommender Systems](recommenders) - examples of how to build various kinds of recommender systems +* [Reinforcement Learning](reinforcement-learning) - a variety of reinforcement learning examples + * [A3C](reinforcement-learning/a3c) + * [DDPG](reinforcement-learning/ddpg) - example of training DDPG for CartPole + * [DQN](reinforcement-learning/dqn) - examples of training DQN and Double DQN to play Atari Games + * [Parallel Advantage-Actor Critic](reinforcement-learning/parallel_actor_critic) +* [RNN Time Major](rnn-time-major) - RNN implementation with Time-major layout +* [Recurrent Neural Net](rnn) - creating recurrent neural networks models using high level `mxnet.rnn` interface +* [Sparse](sparse) - a variety of sparse examples + * [Factorization Machine](sparse/factorization_machine) + * [Linear Classification](sparse/linear_classification) + * [Matrix Factorization](sparse/matrix_factorization) + * [Wide Deep](sparse/wide_deep) +* [Single Shot MultiBox Detector](ssd) - SSD object recognition example +* [Stochastic Depth](stochastic-depth) - implementation of the stochastic depth algorithm +* [Support Vector Machine](svm_mnist) - an SVM example using MNIST +* [Variational Auto Encoder](vae) - implements the Variational Auto Encoder in MXNet using MNIST + +### Other Deep Learning Examples with MXNet + * [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/) @@ -66,7 +123,6 @@ If you want to contribute to this list and the examples, please open a new pull * [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) @@ -142,3 +198,5 @@ If you want to contribute to this list and the examples, please open a new pull * [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 +* [MXNet Model Server](https://github.com/awslabs/mxnet-model-server) - a flexible and easy to use tool for serving Deep Learning models +* [ONNX-MXNet](https://github.com/onnx/onnx-mxnet) - implements ONNX model format support for Apache MXNet ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
