crackcell's Stars
taosdata/TDengine
High-performance, scalable time-series database designed for Industrial IoT (IIoT) scenarios
shap/shap
A game theoretic approach to explain the output of any machine learning model.
doomemacs/doomemacs
An Emacs framework for the stubborn martian hacker
matryer/xbar
Put the output from any script or program into your macOS Menu Bar (the BitBar reboot)
CoatiSoftware/Sourcetrail
Sourcetrail - free and open-source interactive source explorer
spotify/annoy
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
Embedding/Chinese-Word-Vectors
100+ Chinese Word Vectors 上百种预训练中文词向量
Morizeyao/GPT2-Chinese
Chinese version of GPT2 training code, using BERT tokenizer.
py-why/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
interpretml/interpret
Fit interpretable models. Explain blackbox machine learning.
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
microsoft/SPTAG
A distributed approximate nearest neighborhood search (ANN) library which provides a high quality vector index build, search and distributed online serving toolkits for large scale vector search scenario.
hibayesian/awesome-automl-papers
A curated list of automated machine learning papers, articles, tutorials, slides and projects
snowkylin/tensorflow-handbook
简单粗暴 TensorFlow 2 | A Concise Handbook of TensorFlow 2 | 一本简明的 TensorFlow 2 入门指导教程
py-why/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
go-ozzo/ozzo-validation
An idiomatic Go (golang) validation package. Supports configurable and extensible validation rules (validators) using normal language constructs instead of error-prone struct tags.
bytedance/byteps
A high performance and generic framework for distributed DNN training
dev-cafe/cmake-cookbook
CMake Cookbook recipes.
facebookarchive/device-year-class
A library that analyzes an Android device's specifications and calculates which year the device would be considered "high end”.
facebook/fbthrift
Facebook's branch of Apache Thrift, including a new C++ server.
fniessen/org-html-themes
Transform your Org mode files into stunning HTML documents in minutes with our Org mode HTML theme. Elevate your productivity and impress your readers! #orgmode #html #theme #productivity #design
benedekrozemberczki/awesome-fraud-detection-papers
A curated list of data mining papers about fraud detection.
bleach1991/lede
DSXiangLi/CTR
CTR模型代码和学习笔记总结
as/a
A graphical text editor
charles9n/bert-sklearn
a sklearn wrapper for Google's BERT model
vletroye/SynoPackages
Various Synology Packages built with Mods Packager
jujum4n/sgi-enhanced
IRIX/SGI inspired: Theme, Cursor, Icons, Wallpaper for xfce4
vsemenova/orthoml
Code associated with paper: Orthogonal Machine Learning for Demand Estimation: High-Dimensional Causal Inference in Dynamic Panels, Semenova, Goldman, Chernozhukov, Taddy (2017) https://arxiv.org/abs/1712.09988
PacktPublishing/Practical-Artificial-Intelligence-for-A-B-Testing-
Practical Artificial Intelligence for A/B Testing {video], published by Packt