Why am I passionate about this?
I've been teaching math and physics for more than 20 years as a private tutor. During this time, I experimented with different ways to explain concepts to make them easy to understand. I'm a big fan of using concept maps to show the connections between concepts and teaching topics in an integrated manner, including prerequisites and applications. While researching the material for my book, I read dozens of linear algebra textbooks and watched hundreds of videos, looking for the best ways to explain complicated concepts intuitively. I've tried to distill the essential ideas of linear algebra in my book and prepared this list to highlight the books I learned from.
Ivan's book list on textbooks for learning linear algebra
Why did Ivan love this book?
I like Prof. Cohen's book because it includes computational examples based on Python and NumPy to illustrate each concept. This is the way I like to think about linear algebra concepts.
Yes, it's important to understand the formulas and theoretical ideas, but applying linear algebra operations in the real world will always involve some computational platform and not pen and paper. This is the only book I know that shows readers the practical computational linear algebra in parallel with the theory.
The author provides computational notebooks for each chapter on GitHub, which makes it easy to explore all the material from a code-first computational perspective.
1 author picked Linear Algebra as one of their favorite books, and they share why you should read it.
Linear algebra is perhaps the most important branch of mathematics for computational sciences, including machine learning, AI, data science, statistics, simulations, computer graphics, multivariate analyses, matrix decompositions, signal processing, and so on.
The way linear algebra is presented in traditional textbooks is different from how professionals use linear algebra in computers to solve real-world applications in machine learning, data science, statistics, and signal processing. For example, the "determinant" of a matrix is important for linear algebra theory, but should you actually use the determinant in practical applications? The answer may surprise you!
If you are interested in learning the mathematical…
- Coming soon!