[magicmu]

How accessible is a course like this with no prior knowledge of linear algebra? I know it's listed in the pre-reqs, but with a good head for math and lots of calc, is it something that could be picked up along the way? I'm normally pretty bold about stuff like that, but I know it's a core part of deep learning / ML. If it's really necessary, if anyone has any resources for linear algebra run-throughs it would be greatly appreciated!!

[jskonhovd]

I would start with the nano degree program if you don't have any prior knowledge of ML.

Udacity has a Linear Algebra review course, but I don't believe it is public for now. I had taken a linear algebra course before I took the GT ML class, but I wasn't a expert by any means. I don't believe you will need a deep understanding of linear algebra before taking this class. Singular value decomposition might come up. I think if you are familiar with everything in the following pdf you should be fine.

http://minireference.com/static/tutorials/linear_algebra_in_...

If you are motivated, you will do fine. Good luck!

[datawrangler]

Udacity has a couple free linear algebra courses.

One here: https://www.udacity.com/course/linear-algebra-refresher-cour...

And another here: https://www.udacity.com/course/intro-algebra-review--ma004

[magicmu]

Awesome, thanks for the pdf!

[pdm55]

Simple explanation of the basics:

http://www.intmath.com/matrices-determinants/matrix-determin... - matrices

http://www.intmath.com/vectors/vectors-intro.php - vectors

Linear Algebra using Python:

https://www.coursera.org/course/matrix - Coursera: Coding the Matrix

http://codingthematrix.com/ - Website: Coding the Matrix

The simplest software for linear algebra would be GeoGebra, http://www.geogebra.org/. For instance, to enter a matrix just have the spreadsheet view open, enter the numbers, highlight the cells, then choose the option "Create Matrix". To enter a vector start writing "vec" in the input bar at the bottom and intellisense gives you the option to choose "Vector[<Start Point>, <End Point>]". Choose this. Fill it in, for example, "Vector[(-3, 4), (1, 2)]" (Hint: Use Tab to move between options in the input formula, here to move between "<Start Point>" and "<End Point>]".) Voila the vector is drawn! You can even draw a vector with just two clicks in Graphics view, if you first select the "Vectors" tab at the top (the symbol is a line with an arrowhead). The GeoGebra software is really incredible for learning/doing Linear Algebra, Calculus and Statistics. A real godsend.

[stared]

Linear algebra is simple (but ubiquitous - so you need to feel it, not only "sort of follow it").

Some visual matrix operations are here: http://setosa.io/ev/ If you want a beginner textbook, I recommend http://www.matrixanalysis.com/

Again, it is simple, so maybe you can even take course and look up Wikipedia when needed (but for me it is hard to guess you level, current knowledge, etc).

In any case, this ML course assumes some ML knowledge.

[vowelless]

I like this writeup: http://betterexplained.com/articles/linear-algebra-guide/

[magicmu]

Yeah, your last suggestion was what I was leaning towards. Because of the ubiquity of linear algebra, though, this seems like as good a time as any to start getting a good handle on it. Thanks for the resources!

[mrborgen]

Haven taken this course (though I will), but check out Andrew Ng's Coursera's ML course. He gives a crash course in linear algebra. It helped me a lot in my endeavours in ML & neural nets. I also like to learn as I go along, and this intro was just about enough to get started.

[pramodliv1]

I did not find the crash course in Linear Algebra to be useful. I had to complete Strang's course (ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/) before doing Andrew Ng's course , after which most of the course seemed easy.

[aswanson]

Ng is an awesome lecturer, very easy to follow.