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SKU/Artículo: AMZ-B0DBH225KC

Algorithm Recipes Based On Game Theory: For AI Models

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Paperback

Kindle

Paperback

Detalles del producto

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En stock

Peso con empaque:
0.36 kg

Devolución:
Sí

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Nuevo

Producto de:
Amazon

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USA

Sobre este producto

Algorithm Recipes Based On Game Theory: For AI ModelsAuthor: Richard AragonUnlock the Power of Game Theory in AIDiscover the revolutionary approach to artificial intelligence with "Algorithm Recipes Based On Game Theory: For AI Models." Written by renowned AI expert Richard Aragon, this book delves into the fascinating intersection of game theory and AI, offering a collection of innovative algorithms designed to solve complex problems.Why You Should Read This Book:Innovative Approach: Explore how principles from game theory can inspire and enhance AI algorithms, leading to more robust, fair, and efficient solutions.Comprehensive Coverage: Each chapter presents a unique "recipe" with a use case, mathematical foundation, ingredients, preparation instructions, deployment advice, code implementation, and a summary of the algorithm’s novelty and usability.Practical Applications: Learn how to apply these cutting-edge algorithms to real-world problems, including multi-agent systems, feature selection, optimization, anomaly detection, and more.Expert Insights: Benefit from Richard Aragon's deep expertise and practical experience in AI and game theory.Inside the Book:Chapter 1: The Nash Equilibrium Optimizer (NEO)Chapter 2: The Minimax Classifier (MMC)Chapter 3: The Cooperative Multi-Agent Reinforcement Learner (CMARL)Chapter 4: The Shapley Value Feature Selector (SVFS)Chapter 5: The Stackelberg Game Recommender System (SGRS)Chapter 6: The Evolutionary Stable Strategy Neural Network (ESSNN)Chapter 7: The Zero-Sum Game Neural Network Trainer (ZSGNNT)Chapter 8: The Pareto Optimal Multi-Objective Optimizer (POMOO)Chapter 9: The Bayesian Game Anomaly Detector (BGAD)Chapter 10: The Cournot Competition Regression Model (CCRM)Chapter 11: The Evolutionary Game Theory-Based Genetic Algorithm (EGTGA)Chapter 12: The Cooperative Markov Decision Process (CMDP)Chapter 13: The Mixed Strategy Nash Equilibrium Optimizer (MSNEO)Chapter 14: The Cooperative Bargaining Agreement Clustering (CBAC)Chapter 15: The Shapley Value-Based Fair Feature Selection (SVFFS)Who Should Read This Book:AI Researchers and Practitioners: Enhance your toolkit with game theory-inspired algorithms.Data Scientists: Discover innovative methods for model development and optimization.Students and Educators: Gain a comprehensive understanding of the intersection of game theory and AI.Developers: Access practical, ready-to-implement algorithms for real-world applications.Why Choose This Book?"Algorithm Recipes Based On Game Theory: For AI Models" is not just a book; it's a practical guide that bridges the gap between theoretical concepts and real-world applications. Whether you're an AI professional, a data scientist, or a student, this book provides the insights and tools needed to excel in the rapidly evolving field of artificial intelligence.Get your copy today and unlock the power of game theory in AI!

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