In this recipe, we will use TensorFlow to solve two dimensional linear regressions with the matrix inverse method.
Linear regression can be represented as a set of matrix equations, say . Here we are interested in solving the coefficients in matrix x. We have to be careful if our observation matrix (design matrix) A is not square. The solution to solving x can be expressed as . To show this is indeed the case, we will generate two-dimensional data, solve it in TensorFlow, and plot the result.