daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Improved nbviewer documentation.

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Roger Labbe 2015-05-19 20:15:18 -07:00
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README.md
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@ -7,11 +7,7 @@ Reading Online
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Github is now able to render the notebooks directly. The quickest way to view a notebook is to just click on them above. Chapters names are prefaced with numbers to indicate their order 01_gh_filter.ipynb, and so on. J
Alternatively You may access this book via nbviewer at any time by using this address.
[*Read Online Now*](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)
The book is written as a collection of IPython Notebooks, an interactive, browser based system that allows you to combine text, Python, and math into your browser. The website http://nbviewer.org provides an IPython Notebook server that renders notebooks stored at github (or elsewhere). The rendering is done in real time when you load the book. If you read my book today, and then I make a change tomorrow, when you go back tomorrow you will see that change. Perhaps more importantly, the book uses animations to demonstrate how the algorithms perform over time.
The book is written as a collection of IPython Notebooks, an interactive, browser based system that allows you to combine text, Python, and math into your browser. The website http://nbviewer.org provides an IPython Notebook server that renders notebooks stored at github (or elsewhere). The rendering is done in real time when you load the book. You may use [*this nbviewer link*](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb) to access my book via nbviewer. If you read my book today, and then I make a change tomorrow, when you go back tomorrow you will see that change.
The preface available from the link above has all the information in this README and more, so feel free to follow the link now.
@ -19,7 +15,7 @@ The preface available from the link above has all the information in this README
Downloading the book
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However, this book is intended to be interactive and I recommend using it in that form. If you install IPython on your computer and then clone this book you will be able to run all of the code in the book yourself. You can perform experiments, see how filters react to different data, see how different filters react to the same data, and so on. I find this sort of immediate feedback both vital and invigorating. You do not have to wonder "what happens if". Try it and see!
However, this book is intended to be interactive and I recommend using it in that form. It's a little more to set up, but worth it. If you install IPython and some supporting libraries on your computer and then clone this book you will be able to run all of the code in the book yourself. You can perform experiments, see how filters react to different data, see how different filters react to the same data, and so on. I find this sort of immediate feedback both vital and invigorating. You do not have to wonder "what happens if". Try it and see!
The github pages for this project are at https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python You can clone it to your hard drive with the command