Linear Algebra and Learning from Data From Gilbert Strang, the first textbook that teaches linear algebra together with deep learning and neural nets. Gilbert Strang (Author) 9780692196380, Wellesley-Cambridge Press Hardback, published 31 January 2019 446 pages24.2 x 19.6 x 2.5 cm, 0.93 kg 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. 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. Subject Areas: Pattern recognition [UYQP], Machine learning [UYQM], Maths for computer scientists [UYAM], Mathematical modelling [PBWH]
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