01566nam 22002417a 4500999001700000008004100017020002500058082001500083100002000098245004200118260004400160300003500204365001900239504005100258520074600309650001801055650001901073650001601092650003601108650002101144942001201165952014701177 c29581d29581190430b xxu||||| |||| 00| 0 eng d a9780692196380c(hbk) a512.5bSTR aStrang, Gilbert aLinear algebra and learning from data aUSA:bWellesley-Cambridge Press,c2019. axiii, 432 p. :bill. ;c24 cm. aGBPb58.99d00 aIncludes bibliographical references and index. aLinear 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 aDeep learning aLinear algebra aNeural nets aLow rank and compressed sensing aSpecial matrices 2ddccBK 00102ddc406512_500000000000000_STR70939682aDAIICTbDAIICTd2019-04-30eBBCg5639.44l7m2o512.5 STRp031871r2024-01-08s2023-12-26yBK