I want to deeply understand maths related to deep learning and machine learning. I am mentioning my background so someone can suggest me some materials for learning purpose.

Backgroud: Undergrad completed.I have completed deep learning specialization. I can calculate derivation of different things like sigmoid and thanks to deep learning specialization, i also know how actually neural network work. I have also implemented few research paper on my own and open sourced the code.

But, now i want to re-learn calculus, linear algebra and probabilities for better understanding of dl and ml methods. Why we choose sigmoid or tanh, how actually relu is implemented and how actually auto-grad works. things like that.

I searched for some book on calculus and linear algebra but recommendations are to off. Many people recommended Spivak and i started reading it. but starting chapter looks too much dull and only explains theoretically. I want materials where i can understand thing pratically. If possible, then with programming exercises.

please suggest for calculus, LA and Stats.