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Artificial Intelligence: Techniques and Optimization
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0.51 kg
0.51 kg
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Nuevo
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Amazon
Amazon
Sobre este producto
- Comprehensive Introduction to AI: The book starts by providing a solid foundation in the basics of artificial intelligence, discussing its history, development, and potential applications in today’s world.
- Diverse AI Environments: It explains the various types of AI environments, such as fully observable and partially observable environments, and provides insights into how intelligent agents operate in each type.
- Search Algorithms: A detailed explanation of uninformed and informed search algorithms, including Breadth-First Search (BFS), Depth-First Search (DFS), A* Search, and more. These algorithms are vital for solving complex problems in AI.
- Adversarial Search: The book explores adversarial search methods, including game theory approaches like Minimax and Alpha-Beta Pruning, which are essential for AI applications in competitive environments such as gaming.
- Learning Techniques: It covers different learning methods, including supervised, unsupervised, semi-supervised, and reinforcement learning, along with ensemble learning techniques for building more accurate and reliable AI models.
- Neural Networks: A section is dedicated to neural networks and how they are modeled after the human brain to process and learn from data.
- Optimization Algorithms: The book delves into optimization methods, discussing how AI systems can be fine-tuned using algorithms like Genetic Algorithms, Swarm Intelligence, and Evolutionary Computation.
- Practical Applications: Each chapter includes real-world examples and applications, making it easy for readers to understand how these AI techniques are applied in various industries.
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