The full title of the book is "Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning" by Jean Gallier and Jocelyn Quaintance

And at 2,100 pages (seemingly letter sized) it may well be the longest free book I have seen on HN. Wow!

Can you finish 2000 pages of math textbooks in a four year math degree???

Also you seem to arrive at your first ML application after 1500 pages or so.

I’m reminded of a class at my university on “early Christian literature” where the professor announced on the first day that despite the title they would actually start in 3,000 BC.

That should be very doable, shouldn't it be? 2200/(4*365) ~= 1.51 pages per day. Of course, that doesn't exclude weekends and holidays.

5 days a week for 32 weeks a year means 160 days per year, for 640 weeks total. That's like 3 and 1/8th page per day.

Sigh.... I'm not sure what the audience for this book is. Anyone looking to learn the math behind Machine Learning would be much better served learning just what they need and expand their horizon as the need arises. I'm not sure anyone really learns this stuff by just reading a 1000 page book, learning happens nonlinearly, you learn a bit of A, then a bit of C, then a bit of B, and try and connect them together. It always reminds me about the story how Heisenberg didn't know what matrix multiplication once when he came up with his version of quantum mechanics. You don't need to know everything to make an impact.

To say this book is way over my head would be a huge understatement.

What subjects of math would I even need to create the firm foundation needed if I am coming to math with close to zero knowledge.

Can anyone recommend good beginner learning sources.

> firm foundation needed

Foundation to read this book or for a specific topic?

For a book for adult learners I recommend (and am working my way through) Ivan Savov's No Bullshit Guide to Math & Physics. Here's a link to a sample from the author's site. I find it a great resource for brushing up on my math(s) to explore audio DSP.

https://minireference.com/static/excerpts/noBSguide_v5_previ...

Wow thanks for the plug!

Here is also the concept map from the book for people to see what topics are covered in it: https://minireference.com/static/conceptmaps/math_and_physic...

Since you mentioned DSP, you might want to also check out this excerpt on Fourier transformations from the No Bullshit Guide to Linear Algebra: https://minireference.com/static/excerpts/fourier_transforma...

This is really great. It's really awesome to have free access to such resources.