000			nam a22 7a 4500
999			_c29581 _d29581
008			190430b xxu||||| |||| 00| 0 eng d
020			_a9780692196380 _c(hbk)
082			_a512.5 _bSTR
100			_aStrang, Gilbert
245			_aLinear algebra and learning from data
260			_aUSA: _bWellesley-Cambridge Press, _c2019.
300			_axiii, 432 p. : _bill. ; _c24 cm.
365			_aGBP _b58.99 _d00
504			_aIncludes bibliographical references and index.
520			_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
650			_aDeep learning
650			_aLinear algebra
650			_aNeural nets
650			_aLow rank and compressed sensing
650			_aSpecial matrices
942			_2ddc _cBK