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As someone who does teach tutorials as a side gig, I would argue that implementing matrix operations in a tutorial on neural networks is overkill. No matter what the level of the tutorial you always need to draw a line and assume a certain amount of background knowledge and knowing how to use standard tools isn't too much to ask. (yes, I know numpy isn't part of python's standard library, but it comes with pretty much any Python distribution as many other libraries depend on it.)

If we're talking about a longer format, such as a book, then we might consider digging deeper and implementing as much as possible using the barest of Python requirements. Indeed, Joel Grus does implement everything from scratch in his great (although a bit dated) book https://www.amazon.com/Data-Science-Scratch-Principles-Pytho....

EDIT: This is still a work in progress (and relies on numpy and matplotlib), but here is my version: https://github.com/DataForScience/DeepLearning These notebooks are meant as support for a webinar so they might not be the clearest as standalone, but you also have the slides there.

A new edition of Grus comes out next week actually...


Nice! He mentioned he was working on it when I met him at Strata last year, but I didn't know it was coming out already.

Ugh I just bought the old one a week ago.

I’d agree... Outside of very rare circumstances (specialist in numerical linear algebra implementations), my opinion is that implementing matrix operations is something you do once (twice) in your numerical courses to get an intuition for the algorithm, and then never again.

But maybe it’s educational to do once if you never have before.

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