Introduction to Matrix Types in Linear Algebra for Machine Learning

Last Updated on August 9, 2019

A lot of linear algebra is concerned with operations on vectors and matrices, and there are many different types of matrices.

There are a few types of matrices that you may encounter again and again when getting started in linear algebra, particularity the parts of linear algebra relevant to machine learning.

In this tutorial, you will discover a suite of different types of matrices from the field of linear algebra that you may encounter in machine learning.

After completing this tutorial, you will know:

• Square, symmetric, triangular, and diagonal matrices that are much as their names suggest.
• Identity matrices that are all zero values except along the main diagonal where the values are 1.
• Orthogonal matrices that generalize the idea of perpendicular vectors and have useful computational properties.

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.

• Update Feb/2018: Fixed small typo in the equivalence equation for the Orthogonal Matrix.

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