Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, 2020 (Cambridge University Press) - This textbook provides a strong foundation in linear algebra with a direct focus on applications in machine learning, covering vectors, matrices, and systems of linear equations.
Introduction to Linear Algebra, Gilbert Strang, 2023 (Wellesley-Cambridge Press) - A widely respected introductory textbook that clearly explains fundamental linear algebra concepts, including vector and matrix operations, and solving linear systems.
NumPy Reference, NumPy Developers, 2025 - The official documentation for NumPy, essential for understanding how to represent and manipulate vectors and matrices in Python for computational tasks.
Deep Learning (Chapter 2: Linear Algebra), Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - This chapter provides an application-oriented overview of linear algebra, explaining its relevance and common uses in machine learning, particularly in deep learning contexts.