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A Matrix Algebra Approach to Artificial Intelligence 2020 Edition
Contributor(s): Zhang, Xian-Da (Author)

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 ISBN: 9811527725     ISBN-13: 9789811527722
Publisher: Springer
OUR PRICE: $237.49  

Binding Type: Paperback - See All Available Formats & Editions
Published: May 2021
Qty:
Temporarily out of stock - Will ship within 2 to 5 weeks
Additional Information
BISAC Categories:
- Computers | Intelligence (ai) & Semantics
- Computers | Data Processing
- Mathematics | Algebra - Linear
Physical Information: 1.09" H x 6.03" W x 9.03" L (3.17 lbs) 820 pages
 
Descriptions, Reviews, Etc.
Publisher Description:

Matrix algebra plays an important role in many core artificial intelligence (AI) areas, including machine learning, neural networks, support vector machines (SVMs) and evolutionary computation. This book offers a comprehensive and in-depth discussion of matrix algebra theory and methods for these four core areas of AI, while also approaching AI from a theoretical matrix algebra perspective.

The book consists of two parts: the first discusses the fundamentals of matrix algebra in detail, while the second focuses on the applications of matrix algebra approaches in AI. Highlighting matrix algebra in graph-based learning and embedding, network embedding, convolutional neural networks and Pareto optimization theory, and discussing recent topics and advances, the book offers a valuable resource for scientists, engineers, and graduate students in various disciplines, including, but not limited to, computer science, mathematics and engineering.
 
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