[RSchaeffer]

Hey! Thanks for these great courses and materials! How much additional math (beyond high school and introductory college courses) do these courses teach? For example, if I were to take both courses, would I be able to understand the papers published by Surya Ganguli (e.g., The Emergence of Spectral Universality in Deep Networks, Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net)?

[jph00]

Ganguli's papers are at the more "math-y" end of the spectrum when it comes to DL papers. So don't worry if you're finding them a bit unapproachable.

In part 2 of the course I provide quite a bit of advice about how to approach papers in general, and you'll get plenty of practice in reading and implementing papers - but we don't cover the specific math in this particular papers.

My view is the best approach to the math in papers is to generally learn what you need as you get there. It's nearly impossible to know all the math that covers every paper you'll come across, but if you learn the meta-skill of how to learn it on demand, then you'll be just fine! :)

[poppingtonic]

One year of Python, with high school and introductory college courses. Some basic linear algebra, familiarity with logarithms etc.

[RSchaeffer]

I think you misread my question. I'm asking what math students will learn, not what math students should already know.

[bitL]

Gram matrix, some non-linear optimization. Deep Learning is simple, complexity comes from loss function, trying to adjust weights of under-represented categories, different LEGO blocks in building your network and seeing if the particular non-linear optimization works in your case or not. You can go super deep with state-of-art math research in reading about "why do we think Deep Learning works, when it shouldn't", which is mentioned by Jeremy.