git-internals-pdf VS Kalman-and-Bayesian-Filters-in-Python

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".

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.

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.

❯ git log --graph --pretty=oneline --abbrev-commit --all * 150c57d (HEAD -> squash-merge) Squashed commit of the following: | * 535b740 (no-squash-merge) Merge branch 'work-branch' into no-squash-merge |/| | * 1836f1c (work-branch) And more | * 4b84cfe Add more |/ * 16660f8 (main) Add more * 02a154b Initial commit ❯ git cat-file -p no-squash-merge tree 58c1fb22faa444b264e98a5ae4c4ddb07be09697 parent 16660f8b1d1538ed1b55d8533b3ee7feb68e474c parent 1836f1c53221ae701a038bf5ae380770ea911665 author Manuel Odendahl 1653304391 -0400 committer Manuel Odendahl 1653304391 -0400 Merge branch 'work-branch' into no-squash-merge * work-branch: And more Add more squash-merges-considered-harmful on  squash-merge on ☁️ ttc (us-east-1) ❯ git cat-file -p squash-merge tree 58c1fb22faa444b264e98a5ae4c4ddb07be09697 parent 16660f8b1d1538ed1b55d8533b3ee7feb68e474c author Manuel Odendahl 1653304543 -0400 committer Manuel Odendahl 1653304543 -0400 Squashed commit of the following: commit 1836f1c53221ae701a038bf5ae380770ea911665 Author: Manuel Odendahl Date: Mon May 23 07:11:08 2022 -0400 And more commit 4b84cfe11aa51da994448e602e1bc4cc6083d691 Author: Manuel Odendahl Date: Mon May 23 07:11:03 2022 -0400 Add more * ``` {% endraw %} You can see that save that both {% raw %}`squash-merge`{% endraw %} and {% raw %}`no-squash-merge`{% endraw %} point to the exact same tree. The only changed thing is the commit message, and the missing parent in the squash merge. To read more about the underpinnings of git, I can recommend just experimenting with the git command line, and the following resources: - [Building Git by James Coglan](https://shop.jcoglan.com/building-git/) - [Git Internals by Scott Chacon](https://github.com/pluralsight/git-internals-pdf) ## But the history! But Manuel, you say, the history is so much cleaner! To which I counter that it is actually not. If you want to hide the link to the right parent of the non-squash merge (as it is called, the left parent being {% raw %}`main`{% endraw %} ), all you need to do is to hide it. If you use the command-line or a proper tool, use the option to only show first parents. If you only look at the first parent, and configure your git tool to fill in a full log history of the branch into the merge commit message (I personally use the github CLI {% raw %}`gh`{% endraw %} or some git-commit hooks to do it), the squash merge commit is identical to the non squash merge commit. A favorite {% raw %}`git log`{% endraw %} command of mine to quickly look at the history of the main branch, and create a changelog: {% raw %} ```shell > git log --pretty=format:'# %ad %H %s' --date=short --first-parent --reverse # 2022-05-23 02a154bc4f0fa9bca567676d45d136619c076a95 Initial commit # 2022-05-23 16660f8b1d1538ed1b55d8533b3ee7feb68e474c Add more # 2022-05-23 535b740f42e331175f3766c1374116e329a78f7e Merge branch 'work-branch' into no-squash-merge

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.

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

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.

I still very much recommend the Peepcode Git Internals book.

https://github.com/pluralsight/git-internals-pdf/releases

A fantastic interactive introduction to Kalman filters can be found on the following repo:

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...

It explains them from first principles and provides the intuitive rationale for them but doesn't shy away from the math when it feels the student should be ready for it.

No thread on Kalman Filters is complete without a link to this excellent learning resource, a book written as a set of Jupyter notebooks:

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...

That book mentions alpha-beta filters as sort of a younger sibling to full-blown Kalman filters. I recently had need of something like this at work, and started doing a bunch of reading. Eventually I realized that alpha-beta filters (and the whole Kalman family) is very focused on predicting the near future, whereas what I really needed was just a way to smooth historical data.

So I started reading in that direction, came across "double exponential smoothing" which seemed perfect for my use-case, and as I went into it I realized... it's just the alpha-beta filter again, but now with different names for all the variables :(

I can't help feeling like this entire neighborhood of math rests on a few common fundamental theories, but because different disciplines arrived at the same systems via different approaches, they end up sounding a little different and the commonality is obscured. Something about power series, Euler's number, gradient descent, filters, feedback systems, general system theory... it feels to me like there's a relatively small kernel of intuitive understanding at the heart of all that stuff, which could end up making glorious sense of a lot of mathematics if I could only grasp it.

Somebody help me out, here!

This provides an excellent intro that jumps right into code. https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

If you know a bit of Python and you find it sometimes tough to grind through a textbook, take a look here:

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...

Interactive examples programmed in Jupyter notebooks.

Thank you, very good resource! Timely too, as I am revising this topic.

My work is mostly in python. I found this interactive book using Jupyter that explains Kalman filters from first principles.

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...

Try this book

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python And on robotics in general

- Kalman and Bayesian Filters in Python