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Springer - A Matrix Algebra Approach to Artificial Intelligence

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Description

Explore the mathematical foundations of modern computing with A Matrix Algebra Approach to Artificial Intelligence by Springer. This book provides an in-depth study of matrix algebra theory and its direct applications in machine learning, neural networks, support vector machines (SVMs), and evolutionary computation. Structured into two distinct parts, the text first establishes a detailed understanding of matrix algebra fundamentals. The second part transitions into practical applications, demonstrating how these mathematical methods drive core AI areas. This resource is designed for those looking to understand AI from a theoretical matrix algebra perspective, bridging the gap between mathematical theory and advanced computational methods.

Key Features

This Springer publication provides an in-depth discussion of matrix algebra theory and methods for core AI areas.

The text covers essential applications in machine learning, neural networks, support vector machines (SVMs), and evolutionary computation.

The book is organized into two parts, starting with a detailed discussion of matrix algebra fundamentals.

The second section focuses on the practical applications of matrix algebra approaches within the field of artificial intelligence.

Readers gain a unique perspective by approaching artificial intelligence through a theoretical matrix algebra lens.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
23 May 2021
Listed Since
26 April 2021

Barcode

No barcode data available

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