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Linear Algebra and Learning from Data

Gilbert Strang (Massachusetts Institute of Technology)



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Wellesley-Cambridge Press,U.S.
31 January 2019
Mathematics & Sciences; Mathematical modelling; Maths for computer scientists; Machine learning; Pattern recognition
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 matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
By:   Gilbert Strang (Massachusetts Institute of Technology)
Imprint:   Wellesley-Cambridge Press,U.S.
Country of Publication:   United States
Dimensions:   Height: 242mm,  Width: 196mm,  Spine: 25mm
Weight:   930g
ISBN:   9780692196380
ISBN 10:   0692196382
Pages:   446
Publication Date:   31 January 2019
Audience:   College/higher education ,  Professional and scholarly ,  Primary ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active

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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