Introduction to Linear Algebra, Gilbert Strang, 2016 (Wellesley-Cambridge Press) - A foundational textbook covering fundamental concepts of linear algebra, including basis, dimension, linear independence, and spanning properties, with clear explanations and examples.
Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, 2020 (Cambridge University Press)DOI: 10.1017/9781108679989 - Provides a comprehensive and accessible treatment of mathematical concepts essential for machine learning, with dedicated sections on vector spaces, basis, and dimension explained in an ML context.
Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016 (MIT Press) - Chapter 2 offers an accessible introduction to linear algebra concepts crucial for machine learning, including vector spaces, linear independence, basis, and dimension, setting the stage for advanced topics.