| I see a lot of users are learning maths for machine learning. I did the same and here is what I found: I started with 3blue1brown's Youtube course[0] on Linear Algebra and loved it. I had already done a college course on LA, but this made me truly understand what I was doing. MIT OCW Scholar(independent study) course on Linear Algebra by Prof. Strang[1] is really good and is designed for self-study. If you have the time, you could look up Coding the matrix[2] too. I read probability from Mathematics for Computer Science-MIT[3] and also referred Khan Academy[4] and PennState STAT 414/415 [5] for statistics and probability. StatQuest channel[6] on Youtube has handwavy but easy to understand videos on statistics for ML too. The Deep learning book[7] by Ian Goodfellow et al. has a couple of chapters at the beginning that gives you a fairly good idea of the mathematics required to get into Deep learning. Communities like r/AskStatistics and r/statistics on Reddit were really helpful when I got stuck. I also chanced upon Mathematics for Machine Learning[8] book recently and it seems to be good. It has a chapter on optimization that is left out in most books but it skips statistics. [0] - https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2x.... [1] - https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algeb.... [2] - http://codingthematrix.com/ [3] - https://courses.csail.mit.edu/6.042/spring18/mcs.pdf [4] - https://www.khanacademy.org/math/statistics-probability [5] - https://onlinecourses.science.psu.edu/stat414/ [6] - https://www.youtube.com/user/joshstarmer/videos [7] - https://www.deeplearningbook.org [8] - https://mml-book.com Copied from my comment here: https://news.ycombinator.com/item?id=18582022 |