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A Matrix Algebra Approach to Artific...
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Zhang, Xian-Da.
A Matrix Algebra Approach to Artificial Intelligence
Record Type:
Language materials, printed : Monograph/item
Title/Author:
A Matrix Algebra Approach to Artificial Intelligence/ by Xian-Da Zhang.
Author:
Zhang, Xian-Da.
Description:
XXXIV, 820 p. 389 illus.online resource. :
Contained By:
Springer Nature eBook
Subject:
Artificial intelligence. -
Online resource:
https://doi.org/10.1007/978-981-15-2770-8
ISBN:
9789811527708
A Matrix Algebra Approach to Artificial Intelligence
Zhang, Xian-Da.
A Matrix Algebra Approach to Artificial Intelligence
[electronic resource] /by Xian-Da Zhang. - 1st ed. 2020. - XXXIV, 820 p. 389 illus.online resource.
Part 1. Introduction to Matrix Algebra -- Chapter 1. Basic Matrix Computation -- Chapter 2. Matrix Differential -- Chapter 3. Gradient and Optimization -- Chapter 4. Solution of Linear Systems -- Chapter 5. Eigenvalue Decomposition -- Part 2. Artificial Intelligence -- Chapter 6. Machine Learning -- Chapter 7. Neural Networks -- Chapter 8. Support Vector Machines -- Chapter 9. Evolutionary Computation.
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. .
ISBN: 9789811527708
Standard No.: 10.1007/978-981-15-2770-8doiSubjects--Topical Terms:
559380
Artificial intelligence.
LC Class. No.: Q334-342
Dewey Class. No.: 006.3
A Matrix Algebra Approach to Artificial Intelligence
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Part 1. Introduction to Matrix Algebra -- Chapter 1. Basic Matrix Computation -- Chapter 2. Matrix Differential -- Chapter 3. Gradient and Optimization -- Chapter 4. Solution of Linear Systems -- Chapter 5. Eigenvalue Decomposition -- Part 2. Artificial Intelligence -- Chapter 6. Machine Learning -- Chapter 7. Neural Networks -- Chapter 8. Support Vector Machines -- Chapter 9. Evolutionary Computation.
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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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