Documenting My Recent Work in Revisiting Linear Algebra

In this article, I will document some of my recent work in revisiting linear algebra, a subject that has gained renewed importance due to its pivotal role in Artificial Intelligence (AI). As a member of the younger generation, I believe that AI presents a once-in-a-lifetime opportunity for us to participate in and drive technological advancement. While we have witnessed the emergence of computers, the internet, and mobile phones, we were not the primary drivers of those changes, as they occurred during our primary and secondary school years. Now, as active participants in the workforce, we have the exciting prospect of shaping the future of AI.

I have been studying "Linear Algebra For Everyone," a book that takes a unique approach to teaching linear algebra concepts. Unlike proof-based books that often begin with the abstract concept of Vector Spaces, this book introduces concepts such as linear combination, dimensions, independence, rank, and associative law through practical examples involving lengths, angles, dot products, and matrix-vector multiplication. The book presents the operational details of linear algebra first and then reveals the abstract concepts and principles through worked examples. This approach is much more approachable than starting with abstract ideas and deriving the operational details, although both methods have their merits.

Refreshing my knowledge of linear algebra is the first step in my journey towards understanding artificial intelligence. As I progress, I plan to delve deeper into probability, statistics, and optimization, which are essential components of AI.