Sign in

Compare repositories

This page shows the Star History and the facts of 2 to 5 repositories in the Repository Index, side by side. You choose the repositories. The page does not rank them.

Stars gained: From May 8, 2026 to Nov 11, 2026, each from zeroStars gainedFrom May 8, 2026 to Nov 11, 2026, each from zero010KMay 8Nov 11, 2026py-why/EconMLunslothai/hyperlearnrsvp/fecon235rasbt/LLMs-from-scratchmicrosoft/ML-For-Beginners
Stars: The star count at the end of each dayStarsThe star count at the end of each day050K100KApr 25Nov 11, 2026py-why/EconMLunslothai/hyperlearnrsvp/fecon235rasbt/LLMs-from-scratchmicrosoft/ML-For-Beginners

Facts

Fact 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.

unslothai/hyperlearn

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

rsvp/fecon235

Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics

rasbt/LLMs-from-scratch

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

microsoft/ML-For-Beginners

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

Stars 4,8502,5311,283108,25492,132
Net stars, last 30 days +46+7+5+1,880+749
Largest star day +9 on Jun 29, 2026+3 on May 16, 2026+2 on Apr 28, 2026+787 on May 12, 2026+100 on Jun 1, 2026
Milestone reached in the record NoneNoneNone100,000 on Aug 4, 2026None
Forks 83416935016,73422,909
Language Jupyter NotebookJupyter NotebookJupyter NotebookJupyter NotebookJupyter Notebook
License Other licenseAPACHE-2.0Other licenseOther licenseMIT
Created Apr 30, 2018Aug 27, 2018Nov 9, 2014Jul 23, 2023Mar 3, 2021
Last push Nov 9, 2026Nov 19, 2024Jan 20, 2023Nov 9, 2026Nov 7, 2026
Archived NoNoNoNoNo
Health facts: Maintenance 2 of 4 checks (2 without data)1 of 4 checks (2 without data)1 of 4 checks (2 without data)4 of 4 checks4 of 4 checks
Health facts: Releases 2 of 3 checks (1 without data)0 of 3 checks (2 without data)0 of 3 checks (2 without data)0 of 3 checks (1 without data)0 of 3 checks (1 without data)
Health facts: Security practice 0 of 1 check (1 without data)0 of 1 check (1 without data)0 of 1 check (1 without data)0 of 1 check1 of 1 check
Health facts: Community files 1 of 3 checks (2 without data)1 of 3 checks (2 without data)1 of 3 checks (2 without data)1 of 3 checks3 of 3 checks
Commits, last 13 weeks No dataNo dataNo data2978
Weeks with commits, last 52 No dataNo dataNo data35 of 5241 of 52
In the Repository Index since Nov 10, 2026Oct 22, 2026Oct 22, 2026Oct 22, 2026Oct 22, 2026

The facts come from the Repository Host and from the Star History that AwesomeRepos records. A shared topic does not mean that one repository can replace another. Each Health Facts row is one dimension alone. AwesomeRepos does not add them into one score. How each check is defined

Example: oven-sh/bun, denoland/deno, nodejs/node