The primary resource for this course is the free online textbook of Jupyter Notebooks, available on Github. They are full of explanations, code samples, pictures, interesting links, and exercises for you to try. Anyone can view the notebooks online by clicking on the links in the readme Table of Contents. However, to really learn the material, you need to interactively run the code, which requires installing Anaconda on your computer (or an equivalent set up of the Python scientific libraries) and you will need to be able to clone or download the git repo.
Accompanying the notebooks is a playlist of lecture videos, available on YouTube. If you are ever confused by a lecture or it goes too quickly, check out the beginning of the next video, where I review concepts from the previous lecture, often explaining things from a new perspective or with different illustrations.
You can ask questions or share your thoughts and resources using the Computational Linear Algebra category on our fast.ai discussion forums.
This course assumes more background than our Practical Deep Learning for Coders course. It was originally taught during the final term before graduation of the USF Master of Science in Analytics program, and all students had already participated in a “Linear Algebra Bootcamp”. If you are new to linear algebra, I recommend you watch the beautiful 3Blue 1Brown Essence of Linear Algebra video series as preparation.
