Math for machine learning by Zygmunt Zając.
From the post:
Sometimes people ask what math they need for machine learning. The answer depends on what you want to do, but in short our opinion is that it is good to have some familiarity with linear algebra and multivariate differentiation.
Linear algebra is a cornerstone because everything in machine learning is a vector or a matrix. Dot products, distance, matrix factorization, eigenvalues etc. come up all the time.
Differentiation matters because of gradient descent. Again, gradient descent is almost everywhere*. It found its way even into the tree domain in the form of gradient boosting – a gradient descent in function space.
We file probability under statistics and that’s why we don’t mention it here.
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Following this introduction you will find a series of books, MOOCs, etc. on linear algebra, calculus and other math resources.
Pass it along!