A Gentle Introduction to Matrix Operations for Machine Learning

Last Updated on August 9, 2019

Matrix operations are used in the description of many machine learning algorithms.

Some operations can be used directly to solve key equations, whereas others provide useful shorthand or foundation in the description and the use of more complex matrix operations.

In this tutorial, you will discover important linear algebra matrix operations used in the description of machine learning methods.

After completing this tutorial, you will know:

• The Transpose operation for flipping the dimensions of a matrix.
• The Inverse operations used in solving systems of linear equations.
• The Trace and Determinant operations used as shorthand notation in other matrix operations.

Kick-start your project with my new book Linear Algebra for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

A Gentle Introduction to Matrix Operations for Machine Learning
Photo by Andrej, some rights reserved.

Tutorial Overview

This tutorial is divided into 5 parts; they are:

1. Transpose
2. Inversion
3. Trace
4. Determinant
5. Rank