Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;) (by CamDavidsonPilon)
git-internals-pdf
PDF on Git Internals (by pluralsight)
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | git-internals-pdf | |
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30 | 23 | |
27,522 | 2,562 | |
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about 1 year ago | over 2 years ago | |
Jupyter Notebook | Ruby | |
MIT License | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
Posts with mentions or reviews of Probabilistic-Programming-and-Bayesian-Methods-for-Hackers.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-02.
- Probabilistic Programming and Bayesian Methods for Hackers (2013)
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[Q] Bayesian statistics!
Also this is quite nice practical introduction which might help with finding answers to your questions: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
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How many of you have used algebra, calculus, geometry, etc in your business careers/the real world?
This is a good intro to probabilistic programming.
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Suggestions for some best books on computer vision
Probabilistic programming is a nice technique to have up your sleeve.
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Bayes examples and study help
+1 for Statistical Rethinking. Iβm also partial to Bayesian Methods for Hackers.
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β¨ 10 Free Books for Machine Learning & Data Science π
π https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
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Predicting the distribution of a variable rather than a point estimate
Youβre welcome! I would recommend Bayesian Methods for Hackers
- Bayesian Methods for Hackers
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A collaborative book on DeFi
All content is open-source: everyone is free to read, but also to contribute to the book using github. I know of one other book that followed this open-source 'publishing' model and became quite successful eventually through community efforts. I contemplated for a bit to create a book DAO but I think it's going to be overkill :).
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[R] Analysis of Russian vaccine trial outcomes suggests they are lazily faked. Distribution of efficacies across age groups is quite improbable
Jake Vanderplas's Statistics for Hackers presentation is a perfect place to start. Bayesian Methods for Hackers is also very good.
git-internals-pdf
Posts with mentions or reviews of git-internals-pdf.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2025-04-07.
- 20 years of Git. Still weird, still wonderful
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What approach helped you to best learn Git?
For me the Peepcode Git Internals book was a great peek under the hood. I went from "Git has a lot of magical incantations" to "Git is pretty simple and I could probably build a version of it".
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Git as a Beginner
I generally recommend the Peepcode Git Internals book. The first half explains the internals of how Git works, and the second half is a command reference.
- Git book recommendations?
- What Git primitives get SHA-1'd to generate a hash?
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How well do you guys know Git
Once you get the hang of basic Git operations, you should look into how Git works under the hood. Git Internals helped me a lot on this.
- β Squash commits considered harmful β
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How should i go about learning git?
I often recommend the Peepcode Git Internals book. The first half talks about how Git works internally. The second half is a "how to use Git" tutorial. I think understanding the internals (which aren't really that complicated) can really help to demystify Git.
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I was said that I do not know how Git works
Conceptually, Git's not really all that complicated. I always recommend people to read the Peepcode Git Internals book (originally $9, now free): https://github.com/pluralsight/git-internals-pdf/blob/master/drafts/peepcode-git.pdf
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would pulling make sense here?
Check out this page if you haven't yet (alternatively, direct link to the PDF.) I hear it's all good, but the Understanding Git chapter is the one I'd specifically point you to.
What are some alternatives?
When comparing Probabilistic-Programming-and-Bayesian-Methods-for-Hackers and git-internals-pdf you can also consider the following projects:
skbel - SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.
Crafting Interpreters - Repository for the book "Crafting Interpreters"
DataScienceProjects - The code repository for projects and tutorials in R and Python that covers a variety of topics in data visualization, statistics sports analytics and general application of probability theory.
CppCoreGuidelines - The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++
machine-learning-cheat-sheet - Classical equations and diagrams in machine learning
github-cheat-sheet - A list of cool features of Git and GitHub.
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers vs skbel
git-internals-pdf vs Crafting Interpreters
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers vs DataScienceProjects
git-internals-pdf vs CppCoreGuidelines
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers vs machine-learning-cheat-sheet
git-internals-pdf vs github-cheat-sheet