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Coding the Matrix: Linear Algebra through Computer Science Applications

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An engaging introduction to vectors and matrices and the algorithms that operate on them, intended for the student who knows how to program. Mathematical concepts and computational problems are motivated by applications in computer science. The reader learns by "doing," writing programs to implement the mathematical concepts and using them to carry out tasks and explore th An engaging introduction to vectors and matrices and the algorithms that operate on them, intended for the student who knows how to program. Mathematical concepts and computational problems are motivated by applications in computer science. The reader learns by "doing," writing programs to implement the mathematical concepts and using them to carry out tasks and explore the applications. Examples include: error-correcting codes, transformations in graphics, face detection, encryption and secret-sharing, integer factoring, removing perspective from an image, PageRank (Google's ranking algorithm), and cancer detection from cell features. A companion web site, codingthematrix.com provides data and support code. Most of the assignments can be auto-graded online. Over two hundred illustrations, including a selection of relevant "xkcd" comics. Chapters: "The Function," "The Field," "The Vector," "The Vector Space," "The Matrix," "The Basis," "Dimension," "Gaussian Elimination," "The Inner Product," "Special Bases," "The Singular Value Decomposition," "The Eigenvector," "The Linear Program"


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An engaging introduction to vectors and matrices and the algorithms that operate on them, intended for the student who knows how to program. Mathematical concepts and computational problems are motivated by applications in computer science. The reader learns by "doing," writing programs to implement the mathematical concepts and using them to carry out tasks and explore th An engaging introduction to vectors and matrices and the algorithms that operate on them, intended for the student who knows how to program. Mathematical concepts and computational problems are motivated by applications in computer science. The reader learns by "doing," writing programs to implement the mathematical concepts and using them to carry out tasks and explore the applications. Examples include: error-correcting codes, transformations in graphics, face detection, encryption and secret-sharing, integer factoring, removing perspective from an image, PageRank (Google's ranking algorithm), and cancer detection from cell features. A companion web site, codingthematrix.com provides data and support code. Most of the assignments can be auto-graded online. Over two hundred illustrations, including a selection of relevant "xkcd" comics. Chapters: "The Function," "The Field," "The Vector," "The Vector Space," "The Matrix," "The Basis," "Dimension," "Gaussian Elimination," "The Inner Product," "Special Bases," "The Singular Value Decomposition," "The Eigenvector," "The Linear Program"

30 review for Coding the Matrix: Linear Algebra through Computer Science Applications

  1. 4 out of 5

    Randy

    Read through during first session of Coursera course on linear algebra - about to have another go when the class is repeated. So happy the examples are in Phython

  2. 4 out of 5

    Matthew Talbert

    Very good introduction to linear algebra, particularly if you can write code in a modern language like Python.

  3. 4 out of 5

    Meremi

    Great "What is twice read, is commonly better remembered than what is transcribed." Dr. Johnson Play by the rules. Hard work, patience, and persistence bring personal insight and freedom. Peace Great "What is twice read, is commonly better remembered than what is transcribed." Dr. Johnson Play by the rules. Hard work, patience, and persistence bring personal insight and freedom. Peace

  4. 5 out of 5

    David

    I work with a few linear algebra types, so I decided a refresher was in order. Many years ago in college, the course I took worked up through idea of the Gram-Schmidt orthonomalizing process. This means I paid some of my dues, but stopping just shy of where the useful concepts start to kick in (processing data in a smaller/sparse basis, PCA, etc.). I selected this book because of its use of Python, and slowly worked through the content (however, I didn't code up the exercises). The FFT explanati I work with a few linear algebra types, so I decided a refresher was in order. Many years ago in college, the course I took worked up through idea of the Gram-Schmidt orthonomalizing process. This means I paid some of my dues, but stopping just shy of where the useful concepts start to kick in (processing data in a smaller/sparse basis, PCA, etc.). I selected this book because of its use of Python, and slowly worked through the content (however, I didn't code up the exercises). The FFT explanation is quite good. It was nice to see the author clearly state that determinants are good for making mathematical arguments, but are rarely useful in computation. That seems consistent with much in linear algebra, as it's not uncommon for me to be reading an algorithm paper that leads with a linear algebra explanation, but then goes on to say that it could never be computed using such an approach, and falls back on something more practical.

  5. 5 out of 5

    Dien Dang

    Excellent book about linear algebra, especially about its application, the book covers almost all level of linear algebra, suitable for both beginner and experienced. Also, have a much practical code example to understand them deeply.

  6. 5 out of 5

    Tom Emerson

  7. 4 out of 5

    Kevin Jacobs

  8. 5 out of 5

    Evgueni Khanine

  9. 4 out of 5

    Yagoub Elryah

  10. 5 out of 5

    Deepak

  11. 5 out of 5

    S

  12. 5 out of 5

    Clint Kelly

  13. 5 out of 5

    Sandra Kellum

  14. 5 out of 5

    Sarah Samy

  15. 5 out of 5

    Desi Cochrane

  16. 4 out of 5

    John Brew

  17. 4 out of 5

    Jackie Qi

  18. 5 out of 5

    Robert Entenman

  19. 5 out of 5

    Jeremy Howard

  20. 5 out of 5

    Carmen

  21. 4 out of 5

    Leonardo Dos santos pinheiro

  22. 4 out of 5

    satej soman

  23. 5 out of 5

    Brad

  24. 4 out of 5

    Oscar

  25. 5 out of 5

    Robert Chiniquy

  26. 4 out of 5

    Marek

  27. 4 out of 5

    David M. Lundgren

  28. 4 out of 5

    Alex

  29. 5 out of 5

    Jim

  30. 5 out of 5

    Kalyani

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