I recently attended a Q&A session about this chapter and deep learning in general given by Yaroslav Bulatov (Open Ai, previously Google Street View). It's pretty good both for beginners and more advanced people.
'Yaroslav Bulatov (OpenAi) Q&A Deep Learning Book, Chapter 2: Linear Algebra':https://www.youtube.com/watch?v=NMhK2A_N0Nc

The best resource is Gilbert Strang's Linear Algebra course taught MIT. He explains things very clearly and with a lot of simple examples. It's all available on yotube!

If you want get a better base of knowledge, Gilbert Strang's Linear Algebra course taught MIT is on youtube. He explains things very clearly and with a lot of simple examples. I highly recommend his course as well.

'Gilbert Strang MIT Linear Algebra Video Lectures':https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/

You may want to play it at 1.5 or 2x speed though :). There are a lot of lectures, you can skip to the ones you're interested in.

You may want to play it at 1.5 or 2x speed though :).

If you just want some visual intuition behind eigen values and eigen vectors there's a very good blog post:
'Eigen Vectors and Eigen Values explained visually':http://setosa.io/ev/eigenvectors-and-eigenvalues/

where the authors have dynamic visualizations. It's super fun to move things around and observe the effects.

where the authors have some dynamic visualizations so you can move things around and observe the effects.

There are lots of applications of linear algebra:
- Pagerank, the algorithm behind Google's success is based on eigen values and eigen vectors