Linear Algebra for Machine Learning

Linear algebra, via the use of matrices and vectors, along with linear algebra libraries (such as NumPy in Python), allows us to perform a large number of calculations in a more computationally efficient way while using simpler code. Knowing at least the numeric operations of linear algebra is crucial to further understanding what happens in our machine learning models. Although having the geometric intuition behind linear algebra can be incredibly useful in visualizing the operations we will discuss below, it is not required to understand most machine learning algorithms. In this tutorial, we will discuss scalars, vectors, matrices, matrix-matrix addition and subtraction, scalar multiplication and division, matrix-vector multiplication, matrix-matrix multiplication, identity matrices, matrix inverses, and matrix transposes. In addition, we will very briefly discuss some of the geometric intuition behind some of these numeric operations.