I am astounded by how you continue to insist that the book doesn't teach anyone anything, when I have already stated that I learned something from it! And of course the book has equations in it. What did you expect?
None of my math books are as obtuse as it is. The equations are presented on their own without explanations. On that page alone they're using quite a bit of mathematical notation that I, at least, have never seen before and I suspect it's largely unnecessary.
What did I expect? I expected a book that explained the concepts in plain english as well as mathematically. I expected the authors to be mature enough not to heavily decorate every single equation with as much mathematical notation as possible. Sort of like how bad coders make their code hard to read. That's the vibe I'm getting from the book.
I learnt eigen decomposition, Hessians, PCA, backpropagation, CNN, dropout, maxpooling etc.
The page you linked above is the derivation of PCA using linear algebra.
First part derives the encoding matrix from the decoding matrix. 2nd part derives the encoding matrix by minimizing the L2 norm.
If you find the math too heavy, you should take Andrew ngs course at Coursera (not his Stanford lectures, which follow a pattern similar to this book). Or pick up any book targeting programmers, machine learning for hackers etc.
None of my math books are as obtuse as it is. The equations are presented on their own without explanations. On that page alone they're using quite a bit of mathematical notation that I, at least, have never seen before and I suspect it's largely unnecessary.
What did I expect? I expected a book that explained the concepts in plain english as well as mathematically. I expected the authors to be mature enough not to heavily decorate every single equation with as much mathematical notation as possible. Sort of like how bad coders make their code hard to read. That's the vibe I'm getting from the book.