This is exactly where I'm learning from as well.

A couple of friends recommended these:- (Not sure if they are relevant though for deep learning specifically)

1) http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-... 2) https://www.khanacademy.org/math/linear-algebra/vectors_and_...

If anyone knows anything else (relevant to deep learning) could you please share :)

Introduction To Statistical Learning:

http://www-bcf.usc.edu/~gareth/ISL/

Is an excellent statistical learning reference.

Just saying, but if you want to hop onto the ML bandwagon (for instance), then don't bother going over linear algebra or probabilities first, and instead just learn what you need as you go. For example, the first sections of this book are already devoted to getting you on the right track, and it's somewhat standard to do so. And besides, there's no need in learning what are rotation matrices if you won't use them.

For probability/statistics you could also use the MIT Course https://www.edx.org/course/introduction-probability-science-...

Same course if you prefer the classroom lectures http://ocw.mit.edu/courses/electrical-engineering-and-comput...

Or if you want more rigor you can go through these notes that cover the same material but in a more formal way (via sigma algebras and measure theory) http://ocw.mit.edu/courses/electrical-engineering-and-comput...

I'm mostly using Khan Academy at the moment. But I see several people already posted alternatives which is nice to have ;)

* Foundations of Machine Learning

* All of Statistics

* Doing Bayesian Data Analysis

Also the ML specialization on Coursera

I've been going through this series of video lectures on Youtube:

https://www.youtube.com/playlist?list=PL5102DFDC6790F3D0

for a basic "Stats 101" course.

There's also this archived Coursera course. There aren't any active sections to sign up for, but the videos are still available:

https://class.coursera.org/introstats-001

This book looks amazing. Thanks for this.