Hi!
This is my second go-round on this topic. I studied Professor Ng’s Machine Learning course a few years ago, and had to teach myself calculus and linear algebra through Khan academy to make sense of it.
I’d love to get a better intuition for some of the math that’s involved in this course. Do you have recommendations for courses or textbooks that could fill in some of the gaps?
There are many ways to approach this problem. I would recommend starting with probability and statistics and moving on to linear algebra afterwards.
My recommendation is to read two online books that will help you understand these concepts better.
Remember the only way to learn mathematics is to do the mathematics.
Here’s a bibliography thread that links to DL books. There are comments about the ones that are more mathematically oriented. In fact, the main one it highlights is the Goodfellow et alia reference that @Akkefa gave us above.
Here’s another thread that links to online resources about the math behind DL.
I also found this book from Cambridge University to be useful: https://mml-book.github.io/.
This is more around the basic mathematics of ML in general and not specifically around Deep Learning, but you can have a look.
Thanks.