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Name: ph_
Type: User
Bio: Helping people manage AI, machine learning, and analytics risks at hallresearch.ai; GWU assistant prof.
Location: Washington, DC
Blog: hallresearch.ai
Name: ph_
Type: User
Bio: Helping people manage AI, machine learning, and analytics risks at hallresearch.ai; GWU assistant prof.
Location: Washington, DC
Blog: hallresearch.ai
Bias and Fairness Audit Toolkit
A running list of links for AutoML - very unofficial and incomplete
A curated list of awesome responsible machine learning resources.
Some Basic (Hopefully Not Terrible) Data Visualization Rules and Links
Code and materials for Python intro. course.
Short example for creating a correlation graph with Pandas and Gephi.
Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
DNSC 6301 Project_Group 21
Some Data Science Interview Questions (by Me and Former Colleagues at SAS)
An Aggregation of a Few Decent Data Science Quick references
Example code and materials that illustrate applications of SAS machine learning techniques.
Example code and materials that illustrate using neural networks with several hidden layers in SAS.
Example code and materials that illustrate techniques for integrating SAS with popular open source analytics technologies like Python and R.
Original, free sample images.
Materials for GWU DNSC 6279 and DNSC 6290.
Example project for DNSC 6301
Human-Centered ML Presentation for H2O World SF 2019.
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
My first repository on GitHub.
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539
Slides for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539
Slides for JSM 2019 preso on model debugging strategies
Paper and talk from KDD 2019 XAI Workshop
Essay about science and data "science".
Simple package for creating LIMEs for XGBoost
Machine Learning Interpretability Resources
Slides for presentation at NAFSA retreat
Example code and materials to a chunk and process a file using Python mulitprocessing.
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.