Book choices vary based on depth of knowledge and intended application. For foundational and application-driven understanding, titles such as Practical Linear Algebra, Before ML, and Linear Algebra with Applications by Zizler & La Haye serve as strong starting points.
For theoretical exploration at the intermediate stage, Introduction to Linear Algebra and Mathematics for Machine Learning presents well-balanced, concept-driven frameworks. Advanced topics, including numerical analysis and abstract formulations, are best explored through Linear Algebra Done Right, Learning from Data, and Matrix Computations.
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