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Name: Liang Cao
Type: User
Name: Liang Cao
Type: User
My collection of implementations of adaptive filters.
Dynamic Optimization
Collection of Artificial Intelligence projects.
An index of algorithms for learning causality with data
A collection of research materials on explainable AI/ML
Implementation of Log Gaussian Cox Process in Python for Changepoint Detection using GPFlow
A python package with tools to perform causal inference using observational data when the treatment of interest is continuous.
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
Causal inference, graphical models and structure learning with the PC algorithm.
A flexible framework of neural networks for deep learning
数据科学竞赛各种baseline代码、思路分享
Results of the research to reproduce
Curvelet-Transform based fibrillar collagen quantification (CurveAlign and CT-FIRE)
DAIS Website Revamp
Personal data competition experience and solutions
This repository contains implementations and illustrative code to accompany DeepMind publications
Code for paper "A method for detecting causal relationships between industrial alarm variables using Transfer entropy and K2-Algorithm"
Deep neural network aided canonical correlation analysis (DNN-CCA) in Tensorflow and Keras
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.
ecg classfication
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.
Fault Diagnosis of Tennessee Eastman Chemical process using Neural Networks
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
Fit interpretable models. Explain blackbox machine learning.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.