Linear Algebra and Learning from Data

Linear Algebra and Learning from Data by Gilbert Strang

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.

Gilbert Strang is a well-known MIT mathematician who works in the fields of scientific research and other areas of applied mathematics.

SKU	Non disponible
ISBN 13	9780692196380
ISBN 10	0692196382
Titre	Linear Algebra and Learning from Data
Auteur	Gilbert Strang
État	Non disponible
Type de reliure	Hardback
Éditeur	Wellesley-Cambridge Press,U.S.
Année de publication	2019-01-31
Nombre de pages	446
Note de couverture	La photo du livre est présentée à titre d'illustration uniquement. La reliure, la couverture ou l'édition réelle peuvent varier.
Note	Non disponible