AI Algorithms (eBook, ePUB)
Foundations, Applications, and Advancements
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This comprehensive volume offers an in-depth exploration of artificial intelligence algorithms, structured into five core parts. Beginning with foundational concepts, it introduces symbolic and statistical AI, emphasizing mathematical underpinnings such as linear algebra, probability, and optimization. Classical AI techniques like search algorithms and constraint satisfaction are explored in depth before transitioning into the domain of machine learning.In supervised and unsupervised learning chapters, readers gain insights into regression, classification, clustering, and dimensionality reduct...
This comprehensive volume offers an in-depth exploration of artificial intelligence algorithms, structured into five core parts. Beginning with foundational concepts, it introduces symbolic and statistical AI, emphasizing mathematical underpinnings such as linear algebra, probability, and optimization. Classical AI techniques like search algorithms and constraint satisfaction are explored in depth before transitioning into the domain of machine learning.
In supervised and unsupervised learning chapters, readers gain insights into regression, classification, clustering, and dimensionality reduction. More advanced topics such as ensemble methods, neural networks-including CNNs, RNNs, and transformers-are detailed with practical and theoretical rigor. Reinforcement learning is examined through frameworks like MDPs, Q-learning, and policy gradients.
The book further delves into evolutionary and probabilistic algorithms, detailing genetic strategies, swarm intelligence, Bayesian networks, and Monte Carlo methods. Applications in natural language processing and computer vision-covering chatbots, object detection, and GANs-are presented with modern techniques like AutoML, neural architecture search, and transfer learning.
A dedicated section on applications and ethics discusses real-world AI use in healthcare, finance, and robotics, along with the challenges of bias, explainability, and governance. Finally, the book explores future directions: the quest for AGI, the promise of quantum AI, and the transformative impact of AI on labor and society.
Balancing technical depth with clarity, this book serves as a valuable resource for students, practitioners, and researchers seeking a robust understanding of both the fundamentals and frontiers of AI.
In supervised and unsupervised learning chapters, readers gain insights into regression, classification, clustering, and dimensionality reduction. More advanced topics such as ensemble methods, neural networks-including CNNs, RNNs, and transformers-are detailed with practical and theoretical rigor. Reinforcement learning is examined through frameworks like MDPs, Q-learning, and policy gradients.
The book further delves into evolutionary and probabilistic algorithms, detailing genetic strategies, swarm intelligence, Bayesian networks, and Monte Carlo methods. Applications in natural language processing and computer vision-covering chatbots, object detection, and GANs-are presented with modern techniques like AutoML, neural architecture search, and transfer learning.
A dedicated section on applications and ethics discusses real-world AI use in healthcare, finance, and robotics, along with the challenges of bias, explainability, and governance. Finally, the book explores future directions: the quest for AGI, the promise of quantum AI, and the transformative impact of AI on labor and society.
Balancing technical depth with clarity, this book serves as a valuable resource for students, practitioners, and researchers seeking a robust understanding of both the fundamentals and frontiers of AI.
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