I'm a self taught programmer with a very weak maths background. What's the best learning path for me if I want to be able to understand and create ML based applications?
Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares
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Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares
#12How much overlap is there between this and Stanford's EE263? Can someone in the know comment on the differences between what is covered?
Comment below: https://news.ycombinator.com/item?id=18678713
Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares
#13I'm e-learning Linear Algebra right now to have a good math foundation for Machine Learning. I was a History and Sociology major in college - so I didn't take any math. If you are like me, and working off an initial base of high school math, I would recommend the following (all free): Linear Algebra Foundations to Frontiers (UT Austin) Course: https://www.edx.org/course/linear-algebra-foundations-to-fro... Comments:…
> This course [Strang] is apparently the holy grail course for Intro Linear Algebra.
I haven't watched his lectures, but I TA'd a linear algebra course that used his text book, and strongly disliked his presentation. I've heard that's a fairly common reaction actually - it's one of those love it or hate it books. I'm bringing it up because if you (or someone else reading this) turn out to be in the group that doesn't love it, you should not give up on loving linear algebra! You are definitely still allowed to have a different 'holy grail course'!
Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares
#14I'm e-learning Linear Algebra right now to have a good math foundation for Machine Learning. I was a History and Sociology major in college - so I didn't take any math. If you are like me, and working off an initial base of high school math, I would recommend the following (all free): Linear Algebra Foundations to Frontiers (UT Austin) Course: https://www.edx.org/course/linear-algebra-foundations-to-fro... Comments:…
> This course [Strang] is apparently the holy grail course for Intro Linear Algebra. I haven't watched his lectures, but I TA'd a linear algebra course that used his text book, and strongly disliked his presentation. I've heard that's a fairly common reaction actually - it's one of those love it or hate it books. I'm bringing it up because if you (or someone else reading this) turn out to be in the group that doesn't…
Awesome input! Learning isn't linear (tee-hee...)
Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares
#15I'm e-learning Linear Algebra right now to have a good math foundation for Machine Learning. I was a History and Sociology major in college - so I didn't take any math. If you are like me, and working off an initial base of high school math, I would recommend the following (all free): Linear Algebra Foundations to Frontiers (UT Austin) Course: https://www.edx.org/course/linear-algebra-foundations-to-fro... Comments:…
> This course [Strang] is apparently the holy grail course for Intro Linear Algebra. I haven't watched his lectures, but I TA'd a linear algebra course that used his text book, and strongly disliked his presentation. I've heard that's a fairly common reaction actually - it's one of those love it or hate it books. I'm bringing it up because if you (or someone else reading this) turn out to be in the group that doesn't…
Gilbert Strang is probably the best teacher on videos, up there with Dan Boneh.
Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares
#16Earlier quoted context omitted.
Don't forget to review calculus as well. Khan Academy is a good start for learning about single variable calculus ( http://www.khanacademy.org ), but their content on multivariable calculus is a bit lacking (neural networks / deep learning use the concept of the derivatives and the gradient a lot). A good supplement for multivariable calculus would be Terence Parr and Jeremy Howard's article on "All the matrix calcul…
Thanks - I am doing that as well! I've been using MIT OpenCourseware for single variable calculus (and will do the same for multivariable). I fenced the parent post to Linear Algebra to not go too far away from the OP. I will certainly check out the Terrence Parr / Jeremy Howard site, and am super familiar with Khan Academy.
I came across-Calculus Made Easy by Silvanus Thompson,on someones twitter feed. Published in 1910 and far less scary and far more interesting to read than a lot of math text books.
Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares
#17I'm a self taught programmer with a very weak maths background. What's the best learning path for me if I want to be able to understand and create ML based applications?
There's a practical course for this http://www.datasciencecourse.org/lectures/ anything you don't know, like linear algebra, look up the topics here for a 1-2hr crash course https://www.youtube.com/playlist?list=PLm3J0oaFux3aafQm568bl...
There's a playlist for a math background in ML for anybody who wants to try a more rigorous ML course https://www.youtube.com/playlist?list=PL7y-1rk2cCsA339crwXMW... More information, including recommended texts https://canvas.cmu.edu/courses/603/assignments/syllabus but don't let that list of prereqs discourage you, can easily look them up directly. You don't have to understand all of Linear Algebra to do matrix multiplication. There's plenty of ML books, papers and playlists on youtube for a full course in ML from dozens of universities https://www.cs.cmu.edu/~roni/10601/ (click on 2017 lectures)
Note never trust YouTube or any other resource to be around forever, make sure you archive everything before you start taking it as lectures tend to disappear (then seed them for others ^^ )
If you have a really weak background go through this free book, refuse to not be able to complete it https://infinitedescent.xyz/
There's no answers because the author gives thanks to a grad course in evidenced based teaching where he claims the only way to really know something and remember it is to figure it out for yourself. Math stackexchange can help too.
Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares
#18A book on applied linear algebra with a focus on regression and no mention anywhere of the singular value decomposition??
Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares
#19I'm going to complain about this every chance I get. A 2D vector, we generally store as [x, y, 0]. What's the extra 0? The homogeneous coordinate. A 2D point, we generally store as [x, y, 1]. That extra 1 is the homogeneous coordinate, and since it's there, it means "and apply translations!" If I have a 2D transform, I put the translation component in the last row or column, depending on if you pre-multiply or post-m…
I don't follow. What do we gain by moving the homogeneous coordinate from last position to first? I don't understand this: > then it's just a sparse vector! Set the variables you want to! Or this: > [1] is the origin point in any number of dimensions. Could you clarify? Also, I don't think this book even discusses homogeneous coordinates. It would be sort of unusual for this type of general text and the only mention…
I think what's meant is that by putting it first, you can always treat any point in P^n as a point in P^m by just ignoring the extra numbers if m n. That is the point in P^2, [1 x y], can also be regarded as the point [1 x] in P^1, the point [1 x y 0] in P^3, the point [1 x y 0 0] in P^4, etc. This is in contrast to putting it last, where if you have [x y 1] in P^2 and you want the point in P^1 you need to allocate a new list [x 1], etc.
The vector is sparse in the sense that you can regard a point as being an infinitely long list of numbers of which we are sparsely giving only that prefix that is non-zero (like how you can regard a decimal numeral as being an infinitely long list of digits, all the ones that are missing being 0).
[1] is the origin point in any dimension because it is [1] in P^0, [1 0] in P^1, [1 0 0] in P^2, etc.
Re: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares
#20I'm going to complain about this every chance I get. A 2D vector, we generally store as [x, y, 0]. What's the extra 0? The homogeneous coordinate. A 2D point, we generally store as [x, y, 1]. That extra 1 is the homogeneous coordinate, and since it's there, it means "and apply translations!" If I have a 2D transform, I put the translation component in the last row or column, depending on if you pre-multiply or post-m…
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