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Linear Algebra and Learning from Data

Linear Algebra and Learning from Data

£58.99

textbook
  • Date Published: January 2019
  • availability: In stock
  • format: Hardback
  • isbn: 9780692196380
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£ 58.99
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  • 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 marices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.

    • The first textbook designed to teach linear algebra as a tool for deep learning
    • From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra
    • Includes the necessary background from statistics and optimization
    • Explains stochastic gradient descent, the key algorithim of deep learning, in detail
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    Customer reviews

    24th Apr 2019 by 2019TanTenJin

    the book is talking about some items, The functions of deep learning , very good

    Review was not posted due to profanity

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    Product details

    • Date Published: January 2019
    • format: Hardback
    • isbn: 9780692196380
    • length: 446 pages
    • dimensions: 242 x 196 x 25 mm
    • weight: 0.93kg
    • availability: In stock
  • 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 VI. 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.

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    Linear Algebra and Learning from Data

    Gilbert Strang

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  • Author

    Gilbert Strang, Massachusetts Institute of Technology
    Gilbert Strang has been teaching Linear Algebra at Massachusetts Institute of Technology (MIT) for over fifty years. His online lectures for MIT's OpenCourseWare have been viewed over three million times. He is a former President of the Society for Industrial and Applied Mathematics and Chair of the Joint Policy Board for Mathematics. Professor Strang is author of twelve books, including the bestselling classic Introduction to Linear Algebra (2016), now in its fifth edition.

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