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eli5

A library for debugging/inspecting machine learning classifiers and explaining their predictions

feature-selection

很简单的特征选择代码实现。

federated

A framework for implementing federated learning

free-programming-books

:books: Freely available programming books

gbdt-xgboost-lightgbm-catboost

gkmexplain

Accompanying repository for GkmExplain paper

hctsa

Highly comparative time-series analysis code repository

hyperparameter_hunter

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

ibreakdown_article

Materials for paper about iBreakDown

imbalanced-learn

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

it_book

本项目收藏这些年来看过或者听过的一些不错的常用的上千本书籍，没准你想找的书就在这里呢，包含了互联网行业大多数书籍和面试经验题目等等。有人工智能系列（常用深度学习框架TensorFlow、pytorch、keras。NLP、机器学习，深度学习等等），大数据系列(Spark,Hadoop,Scala,kafka等)，程序员必修系列（C、C++、java、数据结构、linux，设计模式、数据库等等）

kaggle-competition-sberbank

Top 1% rankings (22/3270) code sharing for Kaggle competition Sberbank Russian Housing Market: https://www.kaggle.com/c/sberbank-russian-housing-market

keras-resnet

Keras package for deep residual networks

leeml-notes

李宏毅《机器学习》笔记，在线阅读地址：https://datawhalechina.github.io/leeml-notes

lgbm_demonstration

A simple script demonstrate how to use GridSearchCV with LightGBM(LGBM) and early stopping

lightgbm

A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.

load-forecasting-resnet

short-term load forecasting with deep residual networks

lstmvis

Visualization Toolbox for Long Short Term Memory networks (LSTMs)

mc_dcnn

Multi channel deep convolutional neural network for time series classification

medical_referrals_catboost

Applying the Catboost algorithm to classify medical referrals as "denied" or "approved".

meta-learning-papers

Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning

mli-resources

H2O.ai Machine Learning Interpretability Resources

neural-networks-and-deep-learning

Deep learning projects including applications (face recognition, neural style transfer, autonomous driving, sign language reading, music generation, translation, speech recognition and NLP) and theories (CNNs, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, hyperparameter tuning, regularization, optimization, Residual Networks).

pymoo

NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMA-ES

pymrmr

Python3 binding to mRMR Feature Selection algorithm (currently not maintained)

pyod

A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

python

All Algorithms implemented in Python

pytorch_3dcnn_lung

Implementation of AIFT algorithm for lung nodule classification

reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

scikit-learn

scikit-learn: machine learning in Python