Does Linear Algebra Strang Cover Applications In Computer Science?

As someone who's deeply immersed in both math and computer science, I can confidently say that 'Linear Algebra' by Gilbert Strang does touch on applications in computer science, though not as extensively as some specialized texts. The book is a classic for its clear explanations and foundational approach, but if you're looking for heavy CS applications, you might need to supplement it. Strang does discuss concepts like matrix operations, vector spaces, and eigenvalues, which are crucial in areas like machine learning, computer graphics, and data science. For instance, the Singular Value Decomposition (SVD) section is particularly relevant for algorithms in recommendation systems and image compression.

However, the book leans more toward theoretical understanding rather than practical coding or implementation details. If you want a deeper dive into CS applications, pairing Strang with resources like 'Linear Algebra and Its Applications' by David Lay or online courses focused on computational linear algebra would be ideal. Strang’s strength lies in building intuition, which is invaluable when you later apply these concepts to CS problems.

I’ve used Strang’s 'Linear Algebra' as a reference while working on computer vision projects, and while it doesn’t focus exclusively on CS, it lays the groundwork beautifully. The chapters on matrices and linear transformations are gold for understanding how 3D graphics and transformations work. Eigenvalues and eigenvectors, for example, are everywhere in PCA (Principal Component Analysis) and facial recognition algorithms. Strang’s explanations are so intuitive that they make abstract concepts feel tangible. The book doesn’t spell out Python or MATLAB code, but once you grasp the theory, implementing it in CS contexts becomes much easier. I’d recommend it as a first step before jumping into heavier, application-specific material.

Strang’s book is a staple in my shelf because it makes linear algebra feel less like a chore and more like a toolkit. For computer science, it’s especially useful for understanding the math behind neural networks—like how weight matrices and activation functions rely on linear algebra. The chapter on projections clarifies least squares regression, which is foundational in machine learning. While it won’t teach you how to write a GPU shader, it’ll help you understand the math those shaders rely on. It’s not a CS textbook, but it’s a must-read to build the intuition you’ll need later.

If you’re studying CS, Strang’s book is a solid theoretical companion. It covers key concepts like matrix factorizations and orthogonality, which pop up in cryptography and network algorithms. The applications aren’t the focus, but the clarity of the explanations makes it easier to connect the dots when you encounter linear algebra in coding or research. Pair it with hands-on projects to see the CS relevance firsthand.