Linear Algebra for Deep Learning

Last Updated on August 9, 2019

Linear algebra is a field of applied mathematics that is a prerequisite to reading and understanding the formal description of deep learning methods, such as in papers and textbooks.

Generally, an understanding of linear algebra (or parts thereof) is presented as a prerequisite for machine learning. Although important, this area of mathematics is seldom covered by computer science or software engineering degree programs.

In their seminal textbook on deep learning, Ian Goodfellow and others present chapters covering the prerequisite mathematical concepts for deep learning, including a chapter on linear algebra.

In this post, you will discover the crash course in linear algebra for deep learning presented in the de facto textbook on deep learning.

After reading this post, you will know:

• The topics suggested as prerequisites for deep learning by experts in the field.
• The progression through these topics and their culmination.
• Suggestions for how to get the most out of the chapter as a crash course in linear algebra.

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.