git-from-the-bottom-up VS Kalman-and-Bayesian-Filters-in-Python

Here's a great walk through for how Git works from the bottom up: https://jwiegley.github.io/git-from-the-bottom-up/

It's short, easy to understand and you'll understand HEAD.

Very tangential:

Gerrit also stores some of its configs in a git repo. I was setting up a new instance, but couldn't get Admin permissions because the way my auth front-end didn't play well with the docker image's assumptions.

Gerrit already does a lot of its work via non-standard references. For example, you don't push to a branch, `refs/branches/foo`, you push to a separate `refs/for/foo` namespace that creates the review.

Similarly, Group config is stored in the All-Users git repo [1], but in references created after a UUID, in `refs/groups/UU/UUID`.

I ended up having a to exercise the plumbiest of plumbing commands [2] to create a new commit from scratch (from a tree, from the index, from blobs), to update the group ref to add myself to the Administrators group (this, of course, requires a local shell and permissions on the Gerrit host). It was a great way to exercise what I had learned in Git from the Bottom Up [3]

[1] https://gerrit-review.googlesource.com/Documentation/config-...

[2] https://git-scm.com/book/en/v2/Git-Internals-Git-Objects

[3] https://jwiegley.github.io/git-from-the-bottom-up/

I found Git from the Bottom Up helpful. It is very short as well. Then refer to the official book when you want more detail.

I recently came across "Git from the Bottom Up by John Wiegley" (thanks to Coding Blocks podcast), he has a chapter about rebasing: https://jwiegley.github.io/git-from-the-bottom-up/1-Repository/7-branching-and-the-power-of-rebase.html

You won't have to put your entire life on break in order to understand the fundamentals of git and why it works the way it works. Going through https://jwiegley.github.io/git-from-the-bottom-up/ and really understanding the material will take you a couple of hours at max, but will save you a lot of time in the future.

Wanting to understand things before using them is hardly elitism, not sure why you would think that.

Just like you probably don't want to fix bugs without understand the cause, it's hard to use a tool correctly unless you know how the tool works.

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