Artificial Intelligence (eBook, ePUB)
Principles, Techniques, and Frontiers (2025 Edition)
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Discover the Future of AI with This Comprehensive Guide!Hey there, if you're curious about artificial intelligence and want a book that breaks it down without overwhelming you, this is it. "Artificial Intelligence: Principles, Techniques, and Frontiers (2025 Edition)" covers everything from the basics to cutting-edge stuff. It starts with symbolic AI foundations. You'll learn about intelligent agents. History and philosophy of AI are explained. Ethics and safety come right up front. The job market for AI skills is analyzed. Problem-solving uses search algorithms. Uninformed searches like BFS a...
Discover the Future of AI with This Comprehensive Guide!
Hey there, if you're curious about artificial intelligence and want a book that breaks it down without overwhelming you, this is it. "Artificial Intelligence: Principles, Techniques, and Frontiers (2025 Edition)" covers everything from the basics to cutting-edge stuff. It starts with symbolic AI foundations. You'll learn about intelligent agents. History and philosophy of AI are explained. Ethics and safety come right up front. The job market for AI skills is analyzed. Problem-solving uses search algorithms. Uninformed searches like BFS and DFS are detailed. Informed searches include A* and heuristics. Local search covers hill-climbing and genetic algorithms. Constraint satisfaction problems are defined. Backtracking and heuristics solve CSPs. Adversarial search uses minimax. Alpha-beta pruning optimizes it. Logic starts with propositional. First-order logic handles complex knowledge. Ontologies and knowledge graphs represent data. Neuro-symbolic AI bridges old and new. Uncertainty reasoning uses probability. Bayesian networks model beliefs. Exact and approximate inference compute answers. Time-based reasoning includes Markov chains. Hidden Markov models handle sequences. MDPs formalize decisions. Value and policy iteration solve them. Machine learning pipelines preprocess data. Paradigms like supervised and unsupervised are categorized. Evaluation metrics avoid pitfalls. Bias-variance tradeoff is balanced with regularization. Regression uses linear and logistic. k-NN classifies lazily. Naive Bayes handles text. Decision trees split data. Random forests ensemble them. Boosting with XGBoost improves accuracy. SVMs find margins. Unsupervised learning clusters with k-means. Hierarchical and DBSCAN group data. PCA reduces dimensions. Deep learning builds neural nets. Multilayer perceptrons learn patterns. Backpropagation trains them. Optimizers like Adam speed it up. Dropout prevents overfitting. CNNs process images. Architectures like AlexNet classify visuals. Object detection uses YOLO. RNNs and LSTMs sequence data. Transformers revolutionize NLP. Attention mechanisms focus key parts. BERT and GPT handle language. Reinforcement learning explores Q-learning. Deep Q-networks play games. Policy gradients optimize actions. Generative models include VAEs. GANs create fake data. Diffusion models generate images. Advanced topics cover RAG and MoE. Embodied AI agents interact physically. Alignment ensures safe AI.
What sets this book apart is its 2025 focus-other texts feel outdated, skipping real-world updates like the latest AI winters, governance laws, or breakthroughs in neuro-symbolic systems. It weaves ethics into every chapter, not just an add-on, and ties concepts to job skills like prompt engineering or MLOps that employers crave. Unlike rigid academics, it uses conversational case studies, from Klarna's agents to Netflix modeling, making complex ideas stick. Competitors miss this blend of theory, practice, and forward-thinking frontiers like embodied AI or pluralistic alignment, giving you a competitive edge in a fast-evolving field.
Dive deeper into chapters on deep learning frontiers, where transformers power chatbots and generative AI creates art. Real examples from 2025 reports ground the theory. Whether you're a student, pro, or hobbyist, this guide equips you to build, critique, and innovate in AI. It's packed with visuals, code tips, and exercises for hands-on learning.
This book is independently produced and has no affiliation with any board or organization. It is created under nominative fair use for educational purposes.
Hey there, if you're curious about artificial intelligence and want a book that breaks it down without overwhelming you, this is it. "Artificial Intelligence: Principles, Techniques, and Frontiers (2025 Edition)" covers everything from the basics to cutting-edge stuff. It starts with symbolic AI foundations. You'll learn about intelligent agents. History and philosophy of AI are explained. Ethics and safety come right up front. The job market for AI skills is analyzed. Problem-solving uses search algorithms. Uninformed searches like BFS and DFS are detailed. Informed searches include A* and heuristics. Local search covers hill-climbing and genetic algorithms. Constraint satisfaction problems are defined. Backtracking and heuristics solve CSPs. Adversarial search uses minimax. Alpha-beta pruning optimizes it. Logic starts with propositional. First-order logic handles complex knowledge. Ontologies and knowledge graphs represent data. Neuro-symbolic AI bridges old and new. Uncertainty reasoning uses probability. Bayesian networks model beliefs. Exact and approximate inference compute answers. Time-based reasoning includes Markov chains. Hidden Markov models handle sequences. MDPs formalize decisions. Value and policy iteration solve them. Machine learning pipelines preprocess data. Paradigms like supervised and unsupervised are categorized. Evaluation metrics avoid pitfalls. Bias-variance tradeoff is balanced with regularization. Regression uses linear and logistic. k-NN classifies lazily. Naive Bayes handles text. Decision trees split data. Random forests ensemble them. Boosting with XGBoost improves accuracy. SVMs find margins. Unsupervised learning clusters with k-means. Hierarchical and DBSCAN group data. PCA reduces dimensions. Deep learning builds neural nets. Multilayer perceptrons learn patterns. Backpropagation trains them. Optimizers like Adam speed it up. Dropout prevents overfitting. CNNs process images. Architectures like AlexNet classify visuals. Object detection uses YOLO. RNNs and LSTMs sequence data. Transformers revolutionize NLP. Attention mechanisms focus key parts. BERT and GPT handle language. Reinforcement learning explores Q-learning. Deep Q-networks play games. Policy gradients optimize actions. Generative models include VAEs. GANs create fake data. Diffusion models generate images. Advanced topics cover RAG and MoE. Embodied AI agents interact physically. Alignment ensures safe AI.
What sets this book apart is its 2025 focus-other texts feel outdated, skipping real-world updates like the latest AI winters, governance laws, or breakthroughs in neuro-symbolic systems. It weaves ethics into every chapter, not just an add-on, and ties concepts to job skills like prompt engineering or MLOps that employers crave. Unlike rigid academics, it uses conversational case studies, from Klarna's agents to Netflix modeling, making complex ideas stick. Competitors miss this blend of theory, practice, and forward-thinking frontiers like embodied AI or pluralistic alignment, giving you a competitive edge in a fast-evolving field.
Dive deeper into chapters on deep learning frontiers, where transformers power chatbots and generative AI creates art. Real examples from 2025 reports ground the theory. Whether you're a student, pro, or hobbyist, this guide equips you to build, critique, and innovate in AI. It's packed with visuals, code tips, and exercises for hands-on learning.
This book is independently produced and has no affiliation with any board or organization. It is created under nominative fair use for educational purposes.
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