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Linear algebra and learning from data / Gilbert Strang

By:

Strang, Gilbert

Material type: Text

TextPublication details: England Wellesley-Cambridge Press 2019Description: 432 pISBN:

9780692196380

Subject(s):

DDC classification:

512.5 STR-G

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Collection	Shelving location	Call number	Status	Barcode
Books	BITS Pilani Hyderabad	510	General Stack (For lending)	512.5 STR-G (Browse shelf(Opens below))	Available	41840
Books	BITS Pilani Hyderabad	510	General Stack (For lending)	512.5 STR-G (Browse shelf(Opens below))	Available	41793
Books	BITS Pilani Hyderabad	510	General Stack (For lending)	512.5 STR-G (Browse shelf(Opens below))	Available	39952

Total holds: 0

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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.

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