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This comprehensive text on the theory and techniques of graph neural networks takes students, practitioners, and researchers from the basics to the state of the art. It... Læs mere
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This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial... Læs mere
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Transfer Learning through Embedding Spaces provides a brief background on transfer learning and then focus on the idea of transferring knowledge through... Læs mere
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Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It... Læs mere
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Cognitive Computing is a new topic which aims to simulate human thought processes using computers that self-learn... Læs mere
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A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.
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This book introduces artificial neural networks to students and professionals. It covers the theory and applications in statistical learning methods with concrete Python code examples.
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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
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Large-scale machine learning could prove highly beneficial in the study of earth science, a broad, multidisciplinary field of study that generates huge... Læs mere
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Fundamental topics in digital signal processing are covered through the introduction of theory, analytical tables, and applications with simulation tools. The... Læs mere
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This book provides a broad overview of the available machine learning techniques for solving civil engineering problems including drought... Læs mere
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Suitable for students, instructors, researchers, engineers and strategists in this field, this title aims to provide a comprehensive guide on the ideas as well as results on selected topics in communication networks and distributed systems.