Yesterday at ML Conference, which took place this year for the first time, I had a talk on cool bits of calculus and linear algebra that are useful and fun to know if you’re writing code for deep learning and/or machine learning.

Originally, the title was something like “What every interested ML/DL developer should know about matrices and calculus”, but then really I didn’t like the schoolmasterly tone that had, as really what I’ve wanted to convey was the fun and the fascination of it …

So, without further ado, here are the slides and the raw presentation on github.

Thanks for reading!

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