Description

Book Synopsis

Preface.- 1 Vectors.- 2 Inner Product, Orthogonality, Norm.- 3 Matrices.- 4. How Matrices Interact with Inner Products and Norms.- 5 Eigenvalues and Singular Values.- 6 Basics of Optimization.- 7 Introduction to Machine Learning and Data.- 8 Principal Component Analysis.- 9 Graph Theory and Graph-based Learning.- 10 Neural Networks and Deep Learning.- 11 Advanced Optimization.- Bibliography.- Index.

Linear Algebra Data Science and Machine Learning

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    A Hardback by Jeff Calder

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      Book details

      Publisher Springer Nature
      Published 15 August 2025
      ISBN-13 9783031937637
      978-3031937637
      ISBN-10 3031937635
      Also in:
      Algebra

      Description

      Book Synopsis

      Preface.- 1 Vectors.- 2 Inner Product, Orthogonality, Norm.- 3 Matrices.- 4. How Matrices Interact with Inner Products and Norms.- 5 Eigenvalues and Singular Values.- 6 Basics of Optimization.- 7 Introduction to Machine Learning and Data.- 8 Principal Component Analysis.- 9 Graph Theory and Graph-based Learning.- 10 Neural Networks and Deep Learning.- 11 Advanced Optimization.- Bibliography.- Index.

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