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Book Synopsis
Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.

Linear Algebra and Learning from Data

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    Order before 4pm tomorrow for delivery by Mon 15 Jun 2026.

    A Hardback by Gilbert Strang

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      View other formats and editions of Linear Algebra and Learning from Data by Gilbert Strang

      Publisher: Wellesley-Cambridge Press,U.S.
      Publication Date: 31/01/2019
      ISBN13: 9780692196380, 978-0692196380
      ISBN10: 0692196382

      Description

      Book Synopsis
      Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.

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