Matrices and Linear Algebra
In this chapter, we are going to focus on linear algebra, specifically matrices and vectors. Vectors are the natural way to represent much of the data you will encounter as a data scientist, and matrices are the natural way to represent things that we do to that data, that is, transformations of the data.
Like the previous chapter, linear algebra is an absolute core part of the math behind data science, and so it is hugely beneficial to understand some of the intuition behind it. That is what this chapter aims to do, by covering the following topics:
- Inner and outer products of vectors: We will learn about the basic building block operations that we can apply to vectors.
- Matrices as transformations: We will learn about the basic operations involving matrices and what they represent.
- Matrix decompositions: We will learn key methods (eigen-decomposition and Singular Value Decomposition (SVD)) for representing matrices that make them simpler...
