Description: Linear Algebra and Learning from Data Item is new in original manufacturers wrap. 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
Price: 25 USD
Location: Youngstown, Ohio
End Time: 2024-12-04T00:25:44.000Z
Shipping Cost: 6.13 USD
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Item Specifics
Restocking Fee: No
Return shipping will be paid by: Buyer
All returns accepted: Returns Accepted
Item must be returned within: 30 Days
Refund will be given as: Money Back
Subject Area: Mathematics
Book Title: Linear Algebra and Its Applications (5th Edition)
Publication Name: Linear Algebra and Its Applications
MPN: Does not apply
Publisher: Pearson Education
Item Length: 0.4 in
Subject: Algebra / Linear, General
Brand: Unbranded
Publication Year: 2014
Type: Textbook
Format: Hardcover
Language: English
Item Height: 0.4 in
Author: Steven Lay, David Lay, Judi McDonald
Level: Intermediate, Advanced, Business
Item Weight: 43 Oz
Item Width: 0.4 in
Number of Pages: 576 Pages
