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Linear Algebra for Deep Learning: Matrix Algebra (quantstart.com)
131 points by shogunmike 7 months ago | hide | past | web | favorite | 8 comments

This is a nice intro to linear algebra but doesn't really get into how it's related to deep learning. A good read is Vector, Matrix, and Tensor Derivatives[0], which goes into the linear algebra needed in multi-layer neural networks, specifically during the backpropogation phase.

[0] http://cs231n.stanford.edu/vecDerivs.pdf

You're correct - it doesn't get into the deep learning aspect yet.

This article is in fact the second part of a larger planned series. The idea is to present more depth than a "quick refresher" that seems to be common to a lot of blog posts, but far less material than would be found in a 10-week (or single semester) undergraduate course.

Thanks for the Stanford link. I'll check it out.

Awesome! I have some Khan Academy courses queued up to learn linear algebra, but this is more targeted and useful.

The best intro to linear algebra, bar none, is the MIT OpenCourseware Intro to Linear Algebra, recordings of a course taught in 2005 by Prof. Gilbert Strang: https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra...

Indeed - I recommended Strang's book in this article and the previous one.

The Strang book: "Introduction to Linear Algebra" does seem to have quite a few "high quality" 1-star reviews. They mostly seem to cluster around this book not being good for a linear algebra introduction, for someone new to the field. Typical comments:

"It seems the reviewers who think this book is wonderful for non-math majors are math majors!"


...I of course expect that any popular book will have 1 star reviews, especially by people who had a hard time with the material. Anyone have thoughts whether the Strang book is better as a supplement to other texts in coming up to speed on Linear Algebra? Other book recommendations?

I'd highly recommend this set of videos: https://youtu.be/kjBOesZCoqc

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