They refresh or refocus the subjects with a geometric intuition and with some concrete applications in mind; which I found quite useful and beautiful.

https://www.coursera.org/learn/linear-algebra-machine-learni... Is _not_ a good introduction. Ok, I understand. Chapman and Hall/CRC, January 2019. I hope you are just trolling. Fundamentals of Numerical Computation. And most people don't use a lot of algebra in their daily lives. Ugh. But, readers, be warned -- the probabilistic context should not be neglected; eventually should learn that, too. A 2D point, we generally store as [x, y, 1]. I think Stewart and Rosen are pretty mainstream textbooks, so i suspect this problem is very common. Which is, i think, the 'right' way of thinking about the algebraic structure, in the sense that it greatly simplifies all the intricate moving parts of linear algebra. Stephen Boyd and Lieven Vandenberghe. This class is more like a more developed version of 3b1b videos on LA. Awesome input! The fundamental strategy of calculus is to replace a nonlinear function with a tangent line approximation to that function. For example: - Why are limits defined the way they are (with epsilons and deltas)? Packt Publishing, 2016. Linear algebra is relatively easy to understand and used everywhere. I will certainly check out the Terrence Parr / Jeremy Howard site, and am super familiar with Khan Academy. I'd probably opt for one or both of these. https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQ... http://www.datasciencecourse.org/lectures/. Tanmay Bakshi. to \Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares". So in math, when we say "vector" coordinate system is a given, as you explained? (They say this book is "respectable", but I would like to hear your thoughts about it. I wonder if this had anything to do with your experience? Out of curiosity, do you feel you can compete with people who have advanced degrees in more quantitative sciences? ©2020 JuliaLang.org contributors. That's a pretty good list, here are some things I'd add. Apress, November 2019. +1 for archiving these resources. That's what I meant. If so, in what ways? Something else I noticed, vector notation does not specify a coordinate system. > - Why is the chain rule true? Then it is interesting that another theme in the slides is getting close to much of the work in what computer science calls machine learning. without determinants. I kept going because it was addicting, and really an antithesis to my day job at the time (investment banking) - which I felt was corporate / bureaucratic and unintellectual. September 2019. I worked through the entire course in March/April this year. Quality of teaching might have something to do with it. I guess I'm going to have to catch up. No. One of the use cases for homogeneous coordinates is certainly to be able to achieve translation. Packt Publishing, 2015. Read “Lecture 13” in the textbook Numerical Linear Algebra. It would be sort of unusual for this type of general text and the only mention of "homogeneous" in the index is "homogeneous equation.". If it's your first time encountering the topic, you'll likely feel lost or not see the point. Technically the projective coordinate (3,2,1) should be exactly the same as (6,4,2), and every nonzero multiple thereof. It is a second course that Lax used to teach his advanced undergraduates and beginning graduate students at the Courant Institute. My best performance came in organic chemistry, where I looked for question banks (with answer keys) and solved problems extensively, perhaps bordering on obsessively. I'm looking forward to taking a look at Prof Boyd's book. You're not alone. On the plus side, you will get a certificate at the end. What's the best learning path for me if I want to be able to understand and create ML based applications? Packt Publishing, June 2018. Wow - nice stuff. Even the homework problem is boring since there is no specific purpose. Homogeneous coordinates allow for affine transformations to be represented and performed with a matrix multiply. Have a Julia book you want added to this list? I've heard that's a fairly common reaction actually - it's one of those love it or hate it books. There's a still easier derivation of the least squares normal equations based on perpendicular projections -- they might have included that. Anyhow, I second your recommendation! When I transform a vector by that matrix, the 0 in the homogeneous coordinate means translation doesn't apply. I know this as "an element of the standard basis," B = {e_1, e_2, ...), where e_1 = (1,0,0,...), e_2 = (0,1,0,0,...). The concepts will be taught in Julia, the fastest and most productive modern high- level language for numerical computing and machine learning - but can be applied in any language with which the audience is familiar.

It's a fair comment, and probably true. Homogeneous coordinates are for projection, not for selecting translation. Society for Industrial and Applied Mathematics, April 2017. In math, “Adjective X” usually means something more specific than “X”. Part of a broader effort - I committed to learning to code about 3 years ago. His videos look great. If I hadn't an indicator that my final result was wrong, I would have missed out on many learning opportunities, and objectively my performance would have been worse. From the preface: We use only one theoretical concept from linear algebra, linear independence, and only one computational tool, the QR factorization; our approach to most applications relies on only one method, least squares (or some extension).

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