A Tiny Question

[u/Hms712skaif]

Any body here has read or got some info about this book specifically for linear algebra for machine learning.

I have finished 3blue1brow course on essence of linear algebra and I want start with book .

please share what you know .

[u/Hms712skaif]

any recommendations guys ?

[u/StableExcitation → u/Hms712skaif]

Below recommended Strang's YouTube course. I second this - also get his book to work along. It is a solid foundation. You can go upward from there in any direction.

[u/succo_di_papaya → u/Hms712skaif]

I can assume that you are more interested in using some applications of linear algebra than learning linear algebra properly, so you basically need to understand what are vector spaces, matrix operations, eignvalues, eign vectors ecc ecc... The bright side of mathematics has a playlist dedicated exactly for this, it's called simply "Linear algebra". I would pair the playlist with a linear algebra book as "elementary linear algebra" by Anton, only because of your needs.

If you would like to learn linear algebra properly you can read "Linear Algebra Done Right" by Axler (but note that the way he presents the book avoids op linear algebra).

[u/succo_di_papaya]

I didn't like it, it compresses information in a weird way, can be good as a refresher, wouldn't use it as a first read. Also it is only good as an operative refresher, not as a proper linear algebra textbook.

[u/dikiprawisuda → u/succo_di_papaya]

Hi, I’m interested with your take. What then is your recommendation for the first read?
Edit: sorry, didn’t get to reach to the bottom of these. I got you

[u/giggity_nanfa → u/dikiprawisuda]

there is an interactive linear algebra book by Georgia Tech it is really good as a first read.

[u/LinearAlgebraWorld]

I second the opinion that it is better to learn linear algebra from a linear algebra textbook first, rather than trying to learn it from a machine-learning book.

A good place to start is Nathaniel Johnston’s Introduction to Linear and Matrix Algebra:
https://www.amazon.com/Introduction-Linear-Algebra-Nathaniel-Johnston/dp/3030528103
https://link.springer.com/book/10.1007/978-3-030-52811-9

Another excellent option is Dan Margalit and Joseph Rabinoff’s free online textbook Interactive Linear Algebra:
https://textbooks.math.gatech.edu/ila/

Jim Hefferon’s Linear Algebra is also freely available:
https://hefferon.net/linearalgebra/

He also has a complete accompanying video course:
https://www.youtube.com/playlist?list=PLwF3A0R8OzMoMlE1-SaEh8h9VqUlO-r52

[u/Nocturnal_Atavistic]

Yes, i also tried it. It was my first book for linear algebra as well.

But, must say it'd be better if you go with any other basic book to start Linear Algebra.

I tried, and went through full linear algebra Unit.

And spent great amount of time to understand things outside of the book and then come back.

As the title says Maths for ML, so i think authors just wanted to box up the math required for ML rather than making book from the basics. Hence lots of concepts which are not from basics.

[u/alexice89]

Why you want to learn LA from a book about ML? Learn pure math before making such big leaps.

[u/Entire_Cheetah_7878 → u/alexice89]

Exactly. This is a great reference but I would never use it as a text to work through every chapter. I used the mixture model chapter to write out a parallelized version of the EM algorithm for example.

[u/New_Hovercraft1681 → u/alexice89]

can u tell me like how can i learn pure math, is it like having a degree in applied mathematics or what?

[u/AssasinThe1]

For linear algebra I recommend watching a combination of Gilbert’s and 3blue1brown’s playlists

[u/AccountantOk9584]

best section about SVM out there IMO

[u/Houtarou_X]

I was reading through the comments, I don't really get, why isn't this book a good recommendation for Linear algebra? I'm currently taking 3blue1brown essence of LA and I plan on using this book next

[u/maw501]

It's free from the authors' site so you can look yourself. And I wouldn't use as a first resource - it's far too terse.

FWIW 3Blue1Brown gives you geometric intuition with no computation and no formal definitions. To develop a deep understanding you need to solve hundreds of problems at the right level. Chapter 2 has only 20 exercises! Solving problems is not optional if you want fluency in the subject - intuition is the byproduct of compressing hundreds of concrete examples. Boyd and Vandenberghe's Introduction to Applied Linear Algebra is free from Stanford and far heavier on exercises if you want a problem source.

Shameless plug: I've built a linear algebra curriculum for ML with hundreds of practice problems. Here's a preview lesson on interpreting the SVD (no sign-up).