Numpy Beginner's Guide (Update)

Solving linear systems

A matrix transforms a vector into another vector in a linear way. This transformation mathematically corresponds to a system of linear equations. The numpy.linalg function solve() solves systems of linear equations of the form Ax = b, where A is a matrix, b can be a one-dimensional or two-dimensional array, and x is an unknown variable. We will see the dot() function in action. This function returns the dot product of two floating-point arrays.

The dot() function calculates the dot product (see https://www.khanacademy.org/math/linear-algebra/vectors_and_spaces/dot_cross_products/v/vector-dot-product-and-vector-length). For a matrix A and vector b, the dot product is equal to the following sum: