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A Matrix Algebra Approach to Artificial Intelligence - Xian-Da Zhang

A Matrix Algebra Approach to Artificial Intelligence

By: Xian-Da Zhang

eText | 23 May 2020

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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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Non-FictionComputing & I.T.Computer ScienceArtificial IntelligenceDatabasesData Capture & AnalysisMathematicsAlgebraMathematical Theory of ComputationMaths for Computer ScientistsApplied Mathematics

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