A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Python for《Deep Learning》，该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现

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

Deep Learning API and Server in C++11 support for Caffe, Caffe2, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference

Provide an input CSV and a target field to predict, generate a model + code to run it.

[UNMAINTAINED] Automated machine learning for analytics & production

A collection of research papers on decision, classification and regression trees with implementations.

MLBox is a powerful Automated Machine Learning python library.

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

A curated list of gradient boosting research papers with implementations.

Goal of this repo is to provide the solutions of all Data Science Competitions(Kaggle, Data Hack, Machine Hack, Driven Data etc...).

REST web service for the true real-time scoring (<1 ms) of R, Scikit-Learn and Apache Spark models

用户贷款风险预测

AI比赛相关信息汇总

H2O.ai Machine Learning Interpretability Resources

Open solution to the Home Credit Default Risk challenge 🏡

Real time eye tracking for embedded and mobile devices.

Tiny Gradient Boosting Tree

Pure Java implementation of XGBoost predictor for online prediction tasks.

도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.

Machine learning models for time series analysis

AI100文本分类竞赛代码。从传统机器学习到深度学习方法的测试

Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.