Recommend some calculus books for Deep Learning and neural networks. I know what is integration, differentiation, derivates, limits on a based level. I would like to understand on deep level the calculus behind Deep Learning and neural networks.
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There's a free book that focus on the math for machine learning
Mathematics for Machine Learning (2020) by Deisenroth et al
It covers linear algebra, vector calculus, probability theory, statistics and optimisation in the first part and other basic ML topics (like linear regression) in the second.
Deep Learning is a lot about back-propagation and gradient descent, so you'd be most interested in the vector calculus and optimisation chapters.
The famous Deep Learning book by Goodfellow et al. also has several chapters dedicated to the math required for DL.
If you're interested in obtaining a solid knowledge of basic calculus (at the university level), I'd recommend Elementary Analysis - The Theory of Calculus by Ross, which I partially read in the past.
Having said that, any standard textbook in ML or Deep Learning should contain at least a review of the most basic math topics needed to understand the rest of the book. Check e.g. these examples.
nbro
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