Linear Algebra and Learning from Data

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 matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.

ISBN:	9780692196380
Publisher:	Wellesley-Cambridge Press
Imprint:	Wellesley-Cambridge
Published date:	31 Jan 2019
DEWEY:	512.5
DEWEY edition:	23
Language:	English
Number of pages:	xiii, 431
Weight:	924g
Height:	239mm
Width:	204mm
Spine width:	19mm

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