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@ -89,7 +89,8 @@ and appropriate packages are loaded).
To work with codes from some given chapter:
* it is recommended to use a machine with at least 8GB of RAM when working
with the examples in this book;
with the examples in this book (some examples require more RAM, which
is clearly indicated in the book);
* start a fresh Julia session using the `julia --project` command in a folder
containing the downloaded material (or alternatively use Visual Studio Code
to activate the appropriate project environment automatically);
@ -97,10 +98,22 @@ To work with codes from some given chapter:
the codes were prepared in a way that you do not need to restart Julia
when working with material from a single chapter, unless it is explicitly
written in the instructions to restart Julia (some of the codes require this);
when you move to a new chapter start a new Julia session;
* before each code there is a comment allowing you to locate the relevant part
of the book where it is used; if in the code there is a blank line between
consecutive code sections this means that in the book these codes are
separated by the text of the book explaining what the code does
separated by the text of the book explaining what the code does;
## Accompanying materials
There are the following videos that feature material related to this book:
* Analysis of Lichess puzzles database: https://www.twitch.tv/videos/1483517202
(a shortened version of material covered in chapters 8 and 9); also covered
in [this blogpost](https://bkamins.github.io/julialang/2022/05/12/twitch.html);
* Analysis of GitHub developer graph: https://www.twitch.tv/videos/1527593261
(a shortened version of material covered in chapter 12)
## Data used in the book
@ -111,7 +124,7 @@ They are respectively:
<https://github.com/sidooms/MovieTweetings> under MIT license)
* puzzles.csv.bz2 (for chapter 8, available puzzles at
<https://database.lichess.org/>. The data is distributed under
Creative Commons CC0 license)
Creative Commons CC0 license);
* git_web_ml.zip (for chapter 12, available on
Stanford Large Network Dataset Collection website
<https://snap.stanford.edu/data/github-social.html> under GPL-3.0 License)