ISLR VS jupytext

It’s a well known machine learning book. I’ve read through it and done the exercises in R.

https://github.com/melling/ISLR

There’s an edX course from the authors:

https://www.edx.org/course/statistical-learning

I recently discovered R Markdown. Started doing the ISLR examples with it.

https://github.com/melling/ISLR/blob/main/chapter08/08_Lab02...

https://github.com/melling/ISLR/blob/main/chapter08/08_Lab02...

I need to figure out how to better fit images so I don’t have pages with large gaps

Also, you can now embed executable Python in the files.

I’m up to Chapter 6 in ISLR

https://github.com/melling/ISLR

Would Elements of Statistical Learning be my next book? I’ve seen the Bishop book highly recommended too.

https://www.amazon.com/Pattern-Recognition-Learning-Informat...

Jupytext (https://github.com/mwouts/jupytext) has been designed exactly for this

jupytext is your friend.

So if you run your own jupyter server, -jupy+text can be a great workflow : it takes your notebook synchronized with other formats (python file, makdown, ...), so you can edit your py/md file with neovim, and refresh the browser to execute the notebook.

Automatically convert ipynb files to py when saving them on JupyterLab

The format is only partially invented, it follows Jupytext [0], but adds support for cell metadata. There is no obvious way to get that in fenced codeblocks, especially with the ability to spread it over multiple lines so it plays well with version control.

One more consideration is that it's not "Markdown with code blocks interspersed", one might as well use plaintext or AsciiDoc.

Of course there are tradeoffs.. I wish I had more time to work on it.

[0]: https://github.com/gzuidhof/starboard-notebook/blob/master/d...

[1]: https://github.com/mwouts/jupytext

Using jupytext (allows you to open .md files as notebooks) + jupyter gives you pretty much the same experience. The main issue is that the cell's output will be discarded. To fix it, you can use ploomber to generate an output HTML, so the workflow goes like this:

First, the format. The ipynb format does not play nicely with git since it stores the cell's source code and output in the same file. But Jupyter has built-in mechanisms to allow other formats to look like notebooks. For example, here's a library that allows you to store notebooks on a postgres database (I know this isn't practical, but it's a great example). To give more practical advice, jupytext allows you to open .py files as notebooks. So you can develop interactively but in the backend, you're storing .py files.

You can use jupytext to maintain dual .py/.ipynb representation of notebooks and keep both versions in sync:

https://github.com/mwouts/jupytext/blob/main/docs/paired-not...

It works both ways, it can update the .py file each time you save the notebook, or you can edit the .py file and have the jupytext command line tool update the .ipynb.