A first-principles foundation in single- and multivariable calculus and linear algebra, taught for computing: limits, derivatives, integrals, gradients, vectors, matrices, linear systems, eigenvalues and the SVD — by hand and in NumPy. Every idea is connected to the algorithms it powers: gradient descent, PCA, geometric transformations, least-squares regression, and PageRank. This is the quantitative bar behind machine-learning, graphics, and computer-vision work.
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