I'am finishing my undergraduate degree in computer science and despite having had to take some math classes my math ability is still pretty poor. I struggled a lot with it which I believe was due to missing some pieces of knowledge that I needed to know and not seeing the big picture. Now i'am studying neural networks and turns out they require quite some math(eg: the backpropagation algorithm uses the chain rule). Some people say you don't need the math but I don't think I will be able to to understand neural networks completely without it. And even if I could get away with I would probably still need the math later on.
I have about a month that i can dedicate fully to this quest and I'm determined to learn linear algebra and multivariate calculus in this time frame. I will start with linear algebra (doesn't depend on calculus, right?) and I plan on learning from MIT 18.06 video lectures as well as doing the assignments. They have solutions so I should be able to easily track my progress. I also found this course from Berkeley Math 110. Linear Algebra. It doesn't have video lectures but it has more assignments with solutions so I can practice even more. As for textbooks MIT uses "Introduction to Linear Algebra, Fourth Edition, Gilbert Strang" and Berkeley uses "Linear Algebra by S.H. Friedberg,A.L. Insel and L.E. Spence,Fourth Edition". People here seem to say good things about them.
I'am starting this journey tomorrow. In the mean time I'd like to get some advice. Do you have any advice in order to make this process smoother? Are there any other resources I should know about?