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. .
9789811527708
10.1007/978-981-15-2770-8 doi
Artificial intelligence.
Computer science--Mathematics.
Algebras, Linear.
Artificial Intelligence.
Mathematical Applications in Computer Science.
Linear Algebra.
Q334-342 TA347.A78
006.3
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. .
9789811527708
10.1007/978-981-15-2770-8 doi
Artificial intelligence.
Computer science--Mathematics.
Algebras, Linear.
Artificial Intelligence.
Mathematical Applications in Computer Science.
Linear Algebra.
Q334-342 TA347.A78
006.3