nam a22 7a 4500
29581
29581
190430b xxu||||| |||| 00| 0 eng d
9780692196380
(hbk)
512.5
STR
Strang, Gilbert
Linear algebra and learning from data
USA:
Wellesley-Cambridge Press,
2019.
xiii, 432 p. :
ill. ;
24 cm.
GBP
58.99
00
Includes bibliographical references and index.
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 marices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation
Deep learning
Linear algebra
Neural nets
Low rank and compressed sensing
Special matrices
ddc
BK
0
0
ddc
0
512_500000000000000_STR
0
39682
DAIICT
DAIICT
2019-04-30
BBC
5639.44
7
2
512.5 STR
031871
2024-01-08
2023-12-26
BK