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Linear Algebra and Learning from Data
 
Gilbert Strang Massachusetts Institute of Technology
A publication of Wellesley-Cambridge Press
Linear Algebra and Learning from Data
Hardcover ISBN:  978-0-692-19638-0
Product Code:  STRANG/3
List Price: $95.00
AMS Member Price: $76.00
Please note AMS points can not be used for this product
Linear Algebra and Learning from Data
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Linear Algebra and Learning from Data
Gilbert Strang Massachusetts Institute of Technology
A publication of Wellesley-Cambridge Press
Hardcover ISBN:  978-0-692-19638-0
Product Code:  STRANG/3
List Price: $95.00
AMS Member Price: $76.00
Please note AMS points can not be used for this product
  • Book Details
     
     
    The Gilbert Strang Series
    Volume: 32019; 432 pp
    MSC: Primary 15; Secondary 68

    This is a textbook to help readers understand the steps that lead to deep learning. Linear algebra comes first, especially singular values, least squares, and matrix factorizations. Often the goal is a low rank approximation A = CR (column-row) to a large matrix of data to see its most important part. This uses the full array of applied linear algebra, including randomization for very large matrices.

    Then deep learning creates a large-scale optimization problem for the weights solved by gradient descent or better stochastic gradient descent. Finally, the book develops the architectures of fully connected neural nets and of Convolutional Neural Nets (CNNs) to find patterns in data.

    A publication of Wellesley-Cambridge Press. Distributed within the Americas by the American Mathematical Society.

    Readership

    Anyone interested in learning how data is reduced and interpreted by matrix methods.

  • Additional Material
     
     
  • Requests
     
     
    Review Copy – for publishers of book reviews
    Desk Copy – for instructors who have adopted an AMS textbook for a course
    Examination Copy – for faculty considering an AMS textbook for a course
    Accessibility – to request an alternate format of an AMS title
Volume: 32019; 432 pp
MSC: Primary 15; Secondary 68

This is a textbook to help readers understand the steps that lead to deep learning. Linear algebra comes first, especially singular values, least squares, and matrix factorizations. Often the goal is a low rank approximation A = CR (column-row) to a large matrix of data to see its most important part. This uses the full array of applied linear algebra, including randomization for very large matrices.

Then deep learning creates a large-scale optimization problem for the weights solved by gradient descent or better stochastic gradient descent. Finally, the book develops the architectures of fully connected neural nets and of Convolutional Neural Nets (CNNs) to find patterns in data.

A publication of Wellesley-Cambridge Press. Distributed within the Americas by the American Mathematical Society.

Readership

Anyone interested in learning how data is reduced and interpreted by matrix methods.

Review Copy – for publishers of book reviews
Desk Copy – for instructors who have adopted an AMS textbook for a course
Examination Copy – for faculty considering an AMS textbook for a course
Accessibility – to request an alternate format of an AMS title
Please select which format for which you are requesting permissions.