SKU/Artículo: AMZ-B0G7J1N1FM
Optimization Techniques in Artificial Intelligence VOL-2 (AI AND MATH NEW)
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Hardcover
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0.57 kg
0.57 kg
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Amazon
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USA
USA
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- Artificial Intelligence has transformed the way modern systems perceive information, make decisions, and act autonomously in complex environments. From machine learning models that predict consumer behavior to autonomous systems navigating real-world uncertainty, the success of AI systems fundamentally depends on one critical concept: optimization. At its core, Artificial Intelligence is not merely about building intelligent models; it is about optimally learning, reasoning, adapting, and deciding under constraints. This book, Optimization Techniques in Artificial Intelligence, is written with this central philosophy in mind. Optimization serves as the invisible engine driving nearly every AI algorithm. Whether it is minimizing a loss function in machine learning, maximizing expected reward in reinforcement learning, allocating limited resources efficiently, or searching massive solution spaces for near-optimal answers, optimization techniques define the effectiveness, efficiency, and reliability of intelligent systems. Despite its importance, optimization is often taught in a fragmented manner—linear programming in operations research, gradient descent in machine learning, evolutionary algorithms in heuristic search—without a unified perspective that connects theory, algorithms, and real-world AI applications. This book aims to bridge that gap. Authored by Anshuman Mishra, an experienced academician with over 18 years of teaching and research experience in computer science and artificial intelligence, this book presents a comprehensive, structured, and deeply explanatory treatment of optimization techniques tailored specifically for AI. It is designed to serve as a single, authoritative reference for undergraduate and postgraduate students, PhD researchers, competitive exam aspirants (UGC-NET, GATE), and industry professionals working in AI, data science, and intelligent systems. Purpose and Vision of the Book The primary goal of this book is to develop a strong conceptual and mathematical understanding of optimization, followed by practical algorithmic insights and application-oriented discussions. Rather than treating optimization as an isolated mathematical subject, the book consistently demonstrates how optimization principles are embedded in AI workflows—from classical search problems to deep neural networks and evolutionary computation. The book emphasizes four major pillars of optimization in AI:Linear Programming and Classical OptimizationConvex OptimizationGradient-Based OptimizationEvolutionary and Nature-Inspired AlgorithmsEach of these paradigms plays a unique and complementary role in artificial intelligence. Linear programming provides exact and interpretable solutions under linear constraints. Convex optimization offers strong theoretical guarantees and forms the backbone of many machine learning models. Gradient descent and its variants power deep learning and large-scale AI systems. Evolutionary algorithms address non-convex, discontinuous, and highly complex search spaces where traditional methods fail. This book not only explains these techniques individually but also highlights their interconnections, strengths, limitations, and hybrid use cases. Pedagogical Approach This book is written with a learner-centric approach, making it suitable for both classroom teaching and self-study. Each topic is developed gradually, starting from fundamental concepts and progressing toward advanced ideas. Mathematical derivations are presented with clarity and purpose, ensuring that readers understand not just how an algorithm works, but why it works. Key pedagogical features include:Clear definitions and formal problem formulationsStep-by-step explanation of algorithms
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AR$57.219
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