Table of Contents:
  • Deep learning and neural nets
  • Preface and acknowledgements
  • Part I: Highlights of linear algebra
  • Part II: Computations with large matrices
  • Part III: Low rank and compressed sensing
  • Part IV: Special matrices
  • Part V: Probability and statistics
  • Part IV: Optimization
  • Part VII: Learning from data
  • Books on machine learning
  • Eigenvalues and singular values : rank one
  • Codes and algorithms for numerical linear algebra
  • Counting parameters in the basic factorizations
  • Index of authors
  • Index
  • Index of symbols.