BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Linear Algebra and Learning from Data

Linear Algebra and Learning from Data

Linear Algebra and Learning from Data
Stock photo: cover may vary

Linear Algebra and Learning from Data Hardback - 2019

by Strang, Gilbert

Add to wish list
  • New
  • Hardback
  • first
New

Description

Cambridge University Press, 2019-02-28. First Edition. hardcover. New. 7.72x0.98x9.53. Buy with confidence. Excellent Customer Service & Return policy.
Ask the seller a question Add to wish list
A$137.03
Free Delivery within USA
Standard delivery: 5 to 10 days
More delivery options
Dropship order
Ships from Ergodebooks (Texas, United States)

Details

  • Title Linear Algebra and Learning from Data
  • Author Strang, Gilbert
  • Binding Hardback
  • Edition First Edition
  • Condition New
  • Pages 446
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2019-02-28
  • Bookseller's Inventory # DADAX0692196382
  • ISBN 9780692196380 / 0692196382
  • Weight 0.13 lbs (0.06 kg)
  • Size 7.72x0.98x9.53
  • Category Mathematics
  • Quantity available 6

About Ergodebooks Texas, United States

Biblio member since 2005

Our goal is to provide best customer service and good condition books for the lowest possible price. We are always honest about condition of book. We list book only by ISBN # and hence exact book is guaranteed.

Terms of Sale:

We have 30 day return policy.

Browse books from Ergodebooks

Reader reviews for Linear Algebra and Learning from Data

From the publisher

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.
tracking-