For techies diving into machine learning and deep learning, the math can be daunting. While you can get a lot done without being fluent in vector spaces and linear transformations, understanding these conceptually can go a long way to making you be effective. Both by understanding what’s possible, and in interacting with scientists.
We highly recommend the video series by the amazing educator 3blue1brown (a.k.a Grant Sanderson). If you maybe took a linear algebra class a while ago and don’t remember all of it, or even if you haven’t at all, this is a super efficient way to strengthen your grasp of the basics. Or maybe that linear algebra class left you confused — fair chance these videos will explain things more clearly than your professor did. (No offense, professor.)
We’ve collected the entire series into a playlist for you here…
Or if you have more time on your hands and want to go deeper, try a full MOOC, like one of these:
- Matrix Algebra for Engineers by Hong Kong University of Science and Technology
- Linear Algebra Refresher Course by Udacity
- Mathematics for Machine Learning: Linear Algebra by Imperial College London
Have some other suggestions for great material to (re-)learn linear algebra? Leave a comment!
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