I wanted to share with you all this wonderful resource - a video series introducing linear algebra from the ground up: http://www.3blue1brown.com/essence-of-linear-algebra/ . I hope some of you get a chance to watch it - I’d love to hear your thoughts.
Does anyone have any other tips for linear algebra resources they’ve found helpful? Any questions or comments about linear algebra? Let us know! 
I stumbled across this thread when looking around this site for resources for getting better at Linear Algebra. I’ve had the MIT Linear Algebra course by Gilbert Strang on my list of online courses to work through for a long time. This seemed to be the most comprehensive introduction to the subject. The materials that Jeremy shared seem really interesting. I’m wondering if anyone else has come across Linear Algebra materials that are tailored specifically to what you need for applied machine learning / deep learning? I find most courses either too elementary (they just teach the mechanics of matrix and vector multiplication, transposes, etc., and don’t touch on topics like matrix factorization) or too advanced and get too deep into theory too early. I am at a point where I’m comfortable with basic matrix operations but don’t have a solid grasp on more advanced things. Suggestions / resources are appreciated!
I should note: I am interested in the Computational Linear Algebra course as well. I think I probably need to feel more comfortable with the mechanics. Furthermore, my number one goal in learning Linear Algebra at the moment is understanding papers and other mathy deep learning material. Perhaps others can comment on how the CLA course fits into that path.
@rachel has a “all the linear algebra you need for deep learning” talk coming up in a few weeks - hopefully there will be some useful stuff coming out of that