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Image of - A Matrix Algebra Approach To Artificial Intelligence | Paperback
A Matrix Algebra Approach To Artificial Intelligence | Paperback

A Matrix Algebra Approach To Artificial Intelligence | Paperback

by Zhang X.D.

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.  

Highlights

  • binding-icon

    9789811527722

    ISBN:

  • binding-icon

    Zhang X.D.

    Author:

  • binding-icon

    820

    Pages:

  • binding-icon

    1050 gm

    Weight:

  • langauage-icon

    English

    Language:

  • date-icon

    2020

    Year:

  • edition-icon

    N/A

    Edition:

  • binding-icon

    Paperback

    Binding:

₹18592

₹23240

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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