Linear Algebra and Learning from Data

Specificaties
Gebonden, 446 blz. | Engels
Wellesley Information Services | e druk, 2019
ISBN13: 9780692196380
Rubricering
Hoofdrubriek : Wetenschap en techniek
Juridisch :
Wellesley Information Services e druk, 2019 9780692196380
Verwachte levertijd ongeveer 9 werkdagen

Samenvatting

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.

Specificaties

ISBN13:9780692196380
Taal:Engels
Bindwijze:gebonden
Aantal pagina's:446
Verschijningsdatum:27-4-2022

Inhoudsopgave

Deep learning and neural nets; Preface and acknowledgements; Part I. Highlights of Linear Algebra; Part II. Computations with Large Matrices; Part III. Low Rank and Compressed Sensing; Part IV. Special Matrices; Part V. Probability and Statistics; Part VI. Optimization; Part VII. Learning from Data: Books on machine learning; Eigenvalues and singular values; Rank One; Codes and algorithms for numerical linear algebra; Counting parameters in the basic factorizations; Index of authors; Index; Index of symbols.

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        Linear Algebra and Learning from Data