And I was really struck how a number of the headings in this handbook mapped exactly to the headings in my context file. I suspect this will not be the last time I click that link.
As a programmer, there is no larger time saver than having notes for code snippets, configuration file examples, etc.
I used to use Evernote, then I wrote a personal version of Evernote in Clojure that worked really well for me, except everything was just on my primary laptop. G Suite is not great from a privacy standpoint (but I can live with it) but for me wins out for convenience - well worth $12/month.
EDIT: I used to keep Jupiter-lab running on a GPU leased server for machine learning educational projects. If I still did that, as other people here have pointed out, with the new file interface Jupiter-lab would be a good choice, esapecially with some customization to implement a global search to find stuff quickly in all notebooks.
Disclaimer: I have used todoist, emacs org-mode, wunderlist and trialled a dozen other task management programs.
One of the next feature for ReviewNB is a CI pipeline for Jupyter Notebooks on GitHub. The idea is to make it easy for users to specify notebook "tests"/"checks" that can then be run on every change.
Given the nature of Notebooks, it's a bit hard to design CI for it in a clean way, but I appreciate any inputs or use cases that you might want to see fulfilled.
It's also worth checking out the notebooks for Wes McKinney's data science book. Daniel Chen doesn't have the code from his DS book on GitHub, but does have some useful notebooks he uses for workshops.
(I'm a little concerned with the aggressive way he's come at Wes McKinney in posts and on twitter, considering Wes has given a lot of his time working on open source contributions)
If you want some more recs, my two favorites are Chris Albon's Machine Learning with Python Cookbook and Joel Grus' Data Science from Scratch: First Principles with Python
Personally I'd say this is a good book. Sections dealing with Numpy, Pandas and Matplotlib are great.
However, I am hesitant to say the same about ML section. I felt like this book assumes some familiarity with general ML concepts. I also felt like ML chapters progressed a bit fast from beginning to the core of chapter.
In all, book is great. Sections on Numpy, Pandas etc are great. But as for ML section, don't use that section as an introduction/first course for ML.
Which one of us is accessing the wrong repo?
Its easy to make demands on open source code maintainers time. Not all PRs and "tickets" need attention. The maintainer does not owe us anything.
Some ideas / questions:
- The documentation on GH is unreadable like this
- On GH it says "Patent Pending" so is this not open source after that or is that phrase just a joke?
- How is it related to the mentioned Data Science Handbook?