Math to Code — Interactive Numpy tutorial for engineers

Hi everyone,

While taking part 2 of the course, I was inspired by Jeremy’s examples of how seemingly complicated looking equations (like the Frobenius norm) could be easily implemented in Python. To help other engineers learn how to implement math that they see in papers, I created Math to Code:

It walks you from basic operators like square root and absolute value to summations, matrix multiplication, implementing the Frobenius norm, standard deviation, etc…

I would appreciate any feedback on it. It’s also open source here if you want to see how it’s implemented (it uses the Skulpt library under the hood to execute Python / NumPy client-side in JavaScript, which is pretty fun):

There’s also discussion on Hacker News if you want to check it out —


Great idea, got a bit frustrated though after 2 questions, too structured for me, would prefer if could see all the examples at once and jump straight to interesting parts.

Thanks for the feedback! Presenting everything at once would be nice as an option. You might like this resource which has a lot of examples on one page. In JavaScript but generally applicable:

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