Linear Algebra and Learning from Data

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Gilbert StrangCambridge University Press, 1/31/2019EAN 9780692196380, ISBN10: 0692196382Hardcover, 432 pages, 24.2 x 19.6 x 2.5 cmLanguage: EnglishLinear algebra and the foundation s 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 netsPreface and acknowledgementsPart I. Highlights of Linear AlgebraPart II. Computations with Large MatricesPart III. Low Rank and Compressed SensingPart IV. Special MatricesPart V. Probability and StatisticsPart VI. OptimizationPart VII. Learning from DataBooks on machine learningEigenvalues and singular valuesRank OneCodes and algorithms for numerical linear algebraCounting parameters in the basic factorizationsIndex of authorsIndexIndex of symbols. Show more