Linear Algebra and Learning from Data

Focusing on the intersection of linear algebra and deep learning, this textbook by Professor Gilbert Strang offers a comprehensive course that integrates essential mathematical concepts with practical applications in neural networks. It covers key topics such as the four fundamental subspaces, singular value decompositions, and optimization techniques, along with foundational elements of probability and statistics. The text is designed to be both accessible and rigorous, making it an invaluable resource for students eager to understand how linear algebra underpins modern data learning techniques.