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Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our;
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this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings;
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intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning;
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Take the next steps toward mastering deep learning, the machine learning method that's transforming the world around us by the second;
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This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various;
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Here is a solid introduction to graph theory, covering Dirac's theorem on k-connected graphs, Harary-Nashwilliam's theorem on the;
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emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language;
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This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow;
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language processing/text mining, machine/deep learning, semantic web, and knowledge graphs.;
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This book constitutes the proceedings of the 20th International Conference on Web Information Systems Engineering, WISE 2019, held in Hong;
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This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held;
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The paradigm of Graph Rewriting is used very little in the field of Natural Language Processing. But;...
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A class is learning a lesson on making graphs and interpreting data, and the class clown, Logan, has some off-the-wall answers to his;
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This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ;
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This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ;
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in concepts, advanced guide on deep learning that will give you background to innovate in your environment Who This Book Is For If you are a data;
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popularity with recent advances in deep learning. The authors establish a unifying perspective on representation learning techniques developed;
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popularity with recent advances in deep learning. The authors establish a unifying perspective on representation learning techniques developed;
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concentration on advances of deep learning architectures. The book discusses various artificial intelligence (AI) techniques based on deep learning;
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layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts;
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The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and;
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The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and;
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The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and;
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and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an;
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Who This Book Is For Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.;
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rich in examples on deep learning using MATLAB.;
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required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with TensorFlow provides practical, hands-on;
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