- Based on TensorFlow 1.2+ / PyTorch 0.4+ and Python 3.5 / 3.6
- Tutorial https://github.com/tensorflow/models
- Examples https://github.com/aymericdamien/TensorFlow-Examples
- TensorFlow Machine Learning Cookbook https://github.com/nfmcclure/tensorflow_cookbook
- Data science Python notebooks https://github.com/donnemartin/data-science-ipython-notebooks
- PyTorch Tutorial for Deep Learning Researchershttps://github.com/yunjey/pytorch-tutorial
- An Open-Source Package for Network Embedding (NE)https://github.com/thunlp/OpenNE
- RNN from https://github.com/kjw0612/awesome-rnn
- GAN from https://github.com/hwalsuklee/tensorflow-generative-model-collections
- SNN from bioinfo-jku
- SDNE from GEM: Graph Embedding Methods
- GCN from Graph Convolutional Networks
- imbalanced-learn from https://github.com/scikit-learn-contrib/imbalanced-learn
- rulefit from https://github.com/christophM/rulefit
- thunderSVM from https://github.com/Xtra-Computing/thundersvm
- XGBoost from https://github.com/dmlc/xgboost
- LightGBM from https://github.com/Microsoft/lightgbm/
- graph2vec from 200~https://github.com/benedekrozemberczki/graph2vec
- Skope-rules from https://github.com/scikit-learn-contrib/skope-rules