Pymc Resources
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Scikit Learn | 54,502 |  | 18,944 | 6,722 | 7 hours ago | 64 | May 19, 2022 | 2,195 | bsd-3-clause | Python |
| scikit-learn: machine learning in Python | ||||||||||
| Superset | 52,344 |  | 2 | 8 hours ago | 3 | April 29, 2022 | 1,350 | apache-2.0 | TypeScript | |
| Apache Superset is a Data Visualization and Data Exploration Platform | ||||||||||
| Pandas | 38,601 |  | 38,392 | 22,710 | 6 hours ago | 107 | June 23, 2022 | 3,500 | bsd-3-clause | Python |
| Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more | ||||||||||
| Streamlit | 25,142 |  | 17 | 404 | 6 hours ago | 182 | July 27, 2022 | 640 | apache-2.0 | Python |
| Streamlit — A faster way to build and share data apps. | ||||||||||
| Ai Expert Roadmap | 24,033 | 3 months ago | 13 | mit | JavaScript | |||||
| Roadmap to becoming an Artificial Intelligence Expert in 2022 | ||||||||||
| Data Science For Beginners | 19,218 | 12 days ago | 31 | mit | Jupyter Notebook | |||||
| 10 Weeks, 20 Lessons, Data Science for All! | ||||||||||
| Gradio | 18,467 |  | 1 | 21 | 7 hours ago | 355 | July 04, 2022 | 343 | apache-2.0 | Python |
| Create UIs for your machine learning model in Python in 3 minutes | ||||||||||
| Best Of Ml Python | 13,727 | 6 days ago | 19 | cc-by-sa-4.0 | ||||||
| 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly. | ||||||||||
| Ydata Profiling | 10,694 | 9 hours ago | 168 | mit | Python | |||||
| Create HTML profiling reports from pandas DataFrame objects | ||||||||||
| Openrefine | 9,519 | 9 hours ago | 5 | August 22, 2022 | 602 | bsd-3-clause | Java | |||
| OpenRefine is a free, open source power tool for working with messy data and improving it | ||||||||||
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PyMC3 Resources
PyMC3 educational resources, including the PyMC3 port of the following books (original models in STAN/BUGS/JAGS etc,.):
- "Bayesian Modeling and Computation in Python" by Osvaldo A. Martin, Ravin Kumar, Junpeng Lao
- PyMC3 port of the book "Statistical Rethinking" by Richard McElreath (first edition)
- PyMC3 port of the book "Statistical Rethinking" by Richard McElreath (second edition)
- PyMC3 port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers
- PyMC3 port of the book "Bayesian Statistical Methods" by Brian J. Reich and Sujit K. Ghosh
- PyMC3 port of the book "Bayesian Data Analysis" by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari and Donald B. Rubin
- PyMC port of the book "Bayes Rules" by Alicia A. Johnson, Miles Q. Ott, and Mine Dogucu
How to contribute
Thanks wanting to contribute! These resources are a community effort and we, and all future resource users, appreciate your help.
If just starting
- Reading the contributing guide for pymc is a good place to start. The guide will familiarize you with the high level tools and workflow.
- Some of the instructions will differ so read the below steps first.
- In this repo the environments are defined per resource. Look into each directory to find the environment file and use that
- When ready to contribute open a draft PR stating the scope of work as early as possible. This helps avoid duplicate work early.
- If you have further questions don't hesitate to ask on https://discourse.pymc.io/.
License
Unless otherwise stated in the directory containing the codes, all codes are copyrighted by their author(s) under MIT license.
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