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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis methods to solving AI problems. The purpose of this book is to fill the gap between the AI and modern causal analysis for further facilitating the AI revolution. This book is ideal for graduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics and precision medicine. Key Features:Cover three types of neural networks, formulate deep learning as an optimal control problem and use Pontryagins Maximum Principle for network training.Deep learning for nonlinear mediation and instrumental variable causal analysis.Construction of causal networks is formulated as a continuous optimization problem.Transformer and attention are used to encode-decode graphics. RL is used to infer large causal networks.Use VAE, GAN, neural differential equations, recurrent neural network (RNN) and RL to estimate counterfactual outcomes.AI-based methods for estimation of individualized treatment effect in the presence of network interference. Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Da: Revaluation Books, Exeter, Regno Unito
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Editore: Chapman and Hall/CRC 2022-03-08, 2022
ISBN 10: 0367859408 ISBN 13: 9780367859404
Lingua: Inglese
Da: Chiron Media, Wallingford, Regno Unito
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Prima edizione
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Aggiungi al carrelloCondizione: New. Momiao Xiong, is a professor in the Department of Biostatistics and Data Science, University of Texas School of Public Health, and a regular member in the Genetics & Epigenetics (G&E) Graduate Program at The University of Texas MD Anderson.
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Da: Biblios, Frankfurt am main, HESSE, Germania
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Momiao Xiong, is a professor in the Department of Biostatistics and Data Science, University of Texas School of Public Health, and a regular member in the Genetics & Epigenetics (G&E) Graduate Program at The University of Texas MD Anderson.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination.
Editore: Taylor & Francis Ltd Mär 2022, 2022
ISBN 10: 0367859408 ISBN 13: 9780367859404
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 142,70
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Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis methods to solving AI problems. The purpose of this book is to fill the gap between the AI and modern causal analysis for further facilitating the AI revolution. This book is ideal forgraduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics and precision medicine.Key Features:Cover three types of neural networks, formulate deep learning as an optimal control problem and use Pontryagin's Maximum Principle for network training.Deep learning for nonlinear mediation and instrumental variable causal analysis.Construction of causal networks is formulated as a continuous optimization problem.Transformer and attention are used to encode-decode graphics. RL is used to infer large causal networks.Use VAE, GAN, neural differential equations, recurrent neural network (RNN) and RL to estimate counterfactual outcomes.AI-based methods for estimation of individualized treatment effect in the presence of network interference. 368 pp. Englisch.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 171,33
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Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 159,32
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Aggiungi al carrelloBuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis methods to solving AI problems. The purpose of this book is to fill the gap between the AI and modern causal analysis for further facilitating the AI revolution. This book is ideal forgraduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics and precision medicine.Key Features:Cover three types of neural networks, formulate deep learning as an optimal control problem and use Pontryagin's Maximum Principle for network training.Deep learning for nonlinear mediation and instrumental variable causal analysis.Construction of causal networks is formulated as a continuous optimization problem.Transformer and attention are used to encode-decode graphics. RL is used to infer large causal networks.Use VAE, GAN, neural differential equations, recurrent neural network (RNN) and RL to estimate counterfactual outcomes.AI-based methods for estimation of individualized treatment effect in the presence of network interference.