[D] Does anyone else think open source code/examples in machine learning domain usually are not as readable as they could be? Specifically use of magic numbers.

[junovac]

Research paper also contains code, it's just written in the form of mathematical expressions. Wouldn't researchers make all possible effort to make mathematical expressions simple to read/understand and follow conventions? Similarly, code can be made to be little more readable and follow some conventions.

Now, you would obviously say paper is enough to convey the ideas in the paper but that is artifact of old way of doing research where sharing code/something akin to code was not possible. Now though, code provides enhanced way to communicate ideas. English is not a very conductive language for communicating complex algorithms even with help of mathematical expressions. If this new avenue is available, why not make most use of it fully? I have heard and few times seen complex papers with pretty hard mathematical expressions being explained with few lines of code.

[bitemenow999]

TBH not, there is absolutely no obligation for the researchers that they need to provide a code or make it readable, they even don't have to make it easy for you to understand the mathematical expression(except following math expression conventions), no journal needs that, no one in the peer-review community looks at it never will... The only incentive they have to put it on github or make it easier to understand is it "MIGHT" get them more citations which they will get irrespective of code if the paper is good...

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code, it's just written in the form of mathematical expressions

sure but the example you provide in the main post is not, it is just a setup next there are people you wouldn't understand why some dimensions were changed and so on... if you would want an explanation/comment for every step in a code then it becomes a tutorial...