By Pablo Cáceres, PhD student at UW-Madison

I recently completed an article and tutorial about essential linear algebra concepts and methods for applied machine learning with Python.

My article is meant as a reference rather than a comprehensive course, as it does not contain any exercises. If you ever get confused by matrix multiplication, don’t remember what was the L2 norm, or the conditions for linear independence, this can serve as a quick reference. It also a good introduction for people that don’t need a deep understanding of linear algebra, but still want to learn about the fundamentals to read about machine learning or to use pre-packaged machine learning solutions. Further, it is a good source for people that learned linear algebra a while ago and need a refresher.

I took extra care on providing a compelling narrative in an accesible language, and to create intuitive illustrating about complex concepts. My writing style aims to mirror how I would talk to a dear friend to help him/her/they to understand new ideas.

Be aware that it is a long read, as it has around 25,000 words. As a reference, Kafka’s “The Metamorphosis” has 16,473 words, and Hemingway’s “The Old Man and The Sea” has 35,220 words.

The article has been shared hundreds of times on social media.

There are several ways to access the article:

  • My blog (black background) -> Link.
  • This PDF (white background) -> Link.
  • Interactive mode via Binder -> Link. Once Binder builds the online environment, you can find the notebook at notebooks/01-linear-algebra/01-linear-algebra.ipynb
  • The GitHub repository -> Link.