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Linear Algebra and Learning from Data
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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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From Gilbert Strang, the first textbook that teaches linear algebra together with deep learning and neural nets.

About the Author

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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