The matrix calculus you need for deep learning (2018)

Re:The matrix calculus you need for deep learning (2018) by fn-mote on Friday April 02, 2021 @09:11PM (#26677558)

I think that one of the reasons people look for a good exposition is so that they do not _have_ to muddle through.

I know I look for a simple example or explanation that makes complicated cases clear.

This 'explanation' might be correct, but I found the details that would help someone else follow it are missing. Especially someone with a computer science background who considers a m x 1 matrix different from a 1 x m matrix.

I see a sister comment has explained this, but I want to point out that there are plenty of details... (like sister's statement 0b which looks to me more like an exponential function than a linear function) that's why one goes to a carefully written source.

This paper is a carefully written explanation of the basics, ending with many applications of the chain rule. The "matrix product rule" is not mentioned, which is good in my opinion.

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Re:The matrix calculus you need for deep learning (2018) by yellowcake0 on Friday April 02, 2021 @09:57PM (#26677825)

I admit it's a blue collar approach, but it's served me well when my goal is optimization or some such and I just need to get on with it.

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