A Matrix Algebra Approach to Artificial Intelligen
- 11h 17m
- Xian-Da Zhang
- Springer
- 2020
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.
In this Book
-
A Note from the Family of Dr. Zhang
-
List of Notations
-
Basic Matrix Computation
-
Matrix Differential
-
Gradient and Optimization
-
Solution of Linear Systems
-
Eigenvalue Decomposition
-
Machine Learning
-
Neural Networks
-
Support Vector Machines
-
Evolutionary Computation
