rawls238's Stars
twitter/the-algorithm-ml
Source code for Twitter's Recommendation Algorithm
rawls238/time_use_study_chrome_extension
The source code for the time use study chrome extension
berkeley-reclab/RecLab
ssikdar1/DeconstructingTheFilterBubble
jeffgortmaker/pyblp
BLP Demand Estimation with Python
hongleizhang/RSPapers
A Curated List of Must-read Papers on Recommender System.
SafeGraphInc/awesome-safegraph-datascience
A community-supported list of awesome data science resources relevant to SafeGraph data
nytimes/covid-19-data
A repository of data on coronavirus cases and deaths in the U.S.
lucashusted/pystout
A Package To Make Publication Quality Latex Tables From Python Regression Output
karthik/wesanderson
A Wes Anderson color palette for R
paperswithcode/paperswithcode-data
The full dataset behind paperswithcode.com
paperswithcode/sota-extractor
The SOTA extractor pipeline
paperswithcode/sotabench-api
Easily benchmark Machine Learning models on selected tasks and datasets
paperswithcode/sotabench-eval
Easily evaluate machine learning models on public benchmarks
rawls238/programming-tutorial
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.
ssikdar1/ExAnteFilterBubble
pytorch/botorch
Bayesian optimization in PyTorch
facebook/Ax
Adaptive Experimentation Platform
google-deepmind/trfl
TensorFlow Reinforcement Learning
optimizely/javascript-sdk-demo-app
JavaScript (Frontend) Full Stack demo application
maxwshen/iap-cidl
Causal Inference & Deep Learning, MIT IAP 2018
rawls238/Bandits.jl
Implementation of multi-armed bandits in Julia
papers-we-love/papers-we-love
Papers from the computer science community to read and discuss.
lengstrom/fast-style-transfer
TensorFlow CNN for fast style transfer ⚡🖥🎨🖼
StratoDem/pandas-js
Pandas in JavaScript for data analysis and visualization
gurugio/lowlevelprogramming-university
How to be low-level programmer
optimizely/python-sdk-demo-app
Python Full Stack demo application
davidagold/StructuredQueries.jl
Query representations for Julia
fake-name/AutoTriever
Remote client for distributed automated HTTP(s) content fetching.