guide.elm-lang.org VS Kalman-and-Bayesian-Filters-in-Python

If you look at the repo for that guide (https://github.com/evancz/guide.elm-lang.org), the description and README clearly state that this is his book on learning Elm, so for me it makes complete sense that it is in the I-form. Maybe the fact that it's linked from the official Elm page without any mention of that causes a feeling of disconnect for you.

An Introduction to Elm (HTML)

I am continually perplexed how poor the official documentation is for Elm (https://guide.elm-lang.org). I love the language, I really enjoy working with it, but where does one go to see the complete API? In particular right now I'm trying to find more on setting various events and accessibility attributes in forms, and this is all I see on the official docs: https://guide.elm-lang.org/architecture/forms.html. Not even a label example on a form page? How is this considered good documentation for a language that has been around for a decade? Is there some secret handshake I need to learn to get access to more in-depth documentation of the language?

I think a good first step would be getting familiar with functional programming in general. I recommend working through the Elm Guide, which will get you acquainted with functional programming idioms and working with immutable data. Then, move on to an introductory Haskell resources, such as Write Yourself a Scheme in 48 Hours. After that, hit up CodeWars and start solving puzzles in Haskell.

I think The Elm Guide does a very good job.

Check out Elm. Page 4 of the intro guide I linked offers something close, which you could build upon to create what you want there.

My advice is to follow the elm official guide. Anyway, any doubt you may have, ping me (gabber) on Elm official slack or write to #beginners channel!

This guide will help you learn and migrate to Elm with assumption you already know the basics of React. The Elm guide is great and will give you a thorough understanding of everything you need to know, in a good order.

Elm Introduction: https://guide.elm-lang.org/

The official Elm guide

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