000			01256nam a22001817a 4500
008			191024b2019 ||||| |||| 00| 0 eng d
020			_a9780692196380
082			_a512.5 STR-G
100			_aStrang, Gilbert
245			_aLinear algebra and learning from data / _cGilbert Strang
260			_aEngland _bWellesley-Cambridge Press _c2019
300			_a432 p.
365			_aGBP _b58.99
500			_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 matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
650			_aMathematical optimization
650			_aMathematical statistics
650			_aAlgebras, Linear
999			_c54157 _d54157