Kalman and Bayesian Filters in Python
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Kalman and Bayesian Filters in Python
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Re: Kalman and Bayesian Filters in Python
#2I have just started, and will likely take a couple of days to finish, but I already like what I've read so far.
I really appreciate this author (Roger Labbe, yes?) as well as all the other authors of these online interactive ebooks or whatever they're called ("interabooks"?) for taking the time to prepare such comprehensive and informative material, purely to help others. Surely, preparing something like this could not have been easy or quick. The other day there was a fantastic one on digital signal processing, and today this.
Really, a big thanks to all you e-authors out there!
Re: Kalman and Bayesian Filters in Python
#3Just got started on this over the weekend and was really impressed with not only the content but the presentation as well. Really enjoy learning through iPython notebooks (now Jupyter) and can only imagine things getting better in the future.
Re: Kalman and Bayesian Filters in Python
#4Just got started on this over the weekend and was really impressed with not only the content but the presentation as well. Really enjoy learning through iPython notebooks (now Jupyter) and can only imagine things getting better in the future.
My Machine Learning professor uses iPython notebooks to teach the content in a format much like this. I think its a fantastic way to present it. You have working code snippets right next to theoretical LaTeX formulas and all of the text and matplotlib graphs to explain it.
Re: Kalman and Bayesian Filters in Python
#5Author here.
I struggle a lot with the choice of medium. In many senses Jupyter (IPython Notebook) is fanstastic in terms of workflow. With latex+external program+external data+external output it is hard to keep everything in sync. Here, it all happens in one place. And, of course, it should make it easier for the reader. "What happens if I change this constant?" (A normal question with scientific processing). Trivial to find out in the notebook; much more painful with a paper book.
OTOH, latex is mature technology, and I mean that in in the most positive way possible. I don't have to worry that version 1.7 is coming out tomorrow, and that \int will no longer display an integral sign. I would like to make the book much more interactive - go all 'Bret Victor' on it, but at what cost? I just tried to use Plotly, for example, but they don't support the current version of matplotlib; they are skipping a version for whatever reason. I can't expect readers to play the version war just to read a book. Even with core Python+scipy stack I have doomed myself to endless maintenance as new, breaking changes occur. That is not hypothetical; IPython changed the format of the notebooks, and there went a weekend of work. And, with all of that said, I think most are just reading the PDF version, or using the static nbviewer rendering, not running it locally on their machine. Heck, that is how I read the other IPython books - in nbviewer or PDF form. You know what you can do in PDF that you can't do in Jupyter? Search. I can ctrl+f in a PDF and search an entire book; in Jupyter, which has no concept of a 'book', I have to go from notebook to notebook.
Anyway, I welcome ideas on how to approach this. I come from a world of C++ where code I wrote in 1995 is still running today, and still compiles with the latest compilers. I'm sure I'm not approaching this problem optimally due to lack of experience in web based mediums. But I do fear, not unreasonably, that once I move on the book will become essentially inaccessible in just 10-20 years or so.
Re: Kalman and Bayesian Filters in Python
#6I really like the subtle hat-tip to Star Wars with the use of TIE fighters in the graphs.
(see http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesia...)
Re: Kalman and Bayesian Filters in Python
#7I have just started, and will likely take a couple of days to finish, but I already like what I've read so far. I really appreciate this author (Roger Labbe, yes?) as well as all the other authors of these online interactive ebooks or whatever they're called ("interabooks"?) for taking the time to prepare such comprehensive and informative material, purely to help others. Surely, preparing something like this could n…
If you're one of those authors I too would like to thank you. Also I like to toot my own horn and help you. With some help I built ipy_pep8[1]. It helps iPython Notebook authors to make their code pep8 valid. It has already been used to improve "Probabilistic Programming and Bayesian Methods for Hackers".
[1]: https://github.com/bobjansen/ipy_pep8
[2]: http://nbviewer.ipython.org/github/CamDavidsonPilon/Probabil...
Re: Kalman and Bayesian Filters in Python
#8Author here. I struggle a lot with the choice of medium. In many senses Jupyter (IPython Notebook) is fanstastic in terms of workflow. With latex+external program+external data+external output it is hard to keep everything in sync. Here, it all happens in one place. And, of course, it should make it easier for the reader. "What happens if I change this constant?" (A normal question with scientific processing). Trivia…
Wasn't there a way to run the IPython notebook with a javascript kernel in the browser?
At least that is how iocaml notebooks work (which AFAICT are IPython compatible): you have the full OCaml interpreter running as javascript and can edit any cells: http://gazagnaire.org/fuconf14/
This seems to be the Python equivalent: https://github.com/minrk/jskernel
Re: Kalman and Bayesian Filters in Python
#9I have just started, and will likely take a couple of days to finish, but I already like what I've read so far. I really appreciate this author (Roger Labbe, yes?) as well as all the other authors of these online interactive ebooks or whatever they're called ("interabooks"?) for taking the time to prepare such comprehensive and informative material, purely to help others. Surely, preparing something like this could n…
If you're one of those authors I too would like to thank you. Also I like to toot my own horn and help you. With some help I built ipy_pep8[1]. It helps iPython Notebook authors to make their code pep8 valid. It has already been used to improve "Probabilistic Programming and Bayesian Methods for Hackers". [1]: https://github.com/bobjansen/ipy_pep8 [2]: http://nbviewer.ipython.org/github/CamDavidsonPilon/Probabil...
Sweet tool; I didn't know about it. I will be running it tonight against my source.
Re: Kalman and Bayesian Filters in Python
#10Author here. I struggle a lot with the choice of medium. In many senses Jupyter (IPython Notebook) is fanstastic in terms of workflow. With latex+external program+external data+external output it is hard to keep everything in sync. Here, it all happens in one place. And, of course, it should make it easier for the reader. "What happens if I change this constant?" (A normal question with scientific processing). Trivia…
Wasn't there a way to run the IPython notebook with a javascript kernel in the browser? At least that is how iocaml notebooks work (which AFAICT are IPython compatible): you have the full OCaml interpreter running as javascript and can edit any cells: http://gazagnaire.org/fuconf14/ This seems to be the Python equivalent: https://github.com/minrk/jskernel
Jupyter supports all kinds of kernels now. R, Julia, Javascript, Python, Haskell, Ruby, bash, it goes on and on. I am just giddy in love with the Jupyter project, despite the pain points about the current pace of changes.