Applied graph data science (17 Ergebnisse)
Sprache: Englisch
Verlag: Orange Education Pvt Ltd 2024
Serie: Graph & Big Data Analytics — Applied Path, Buch 1 von 2. Buch 1 von 2 - Graph & Big Data Analytics — Applied Path
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Sprache: Englisch
Verlag: Orange Education Pvt Ltd 2024
Serie: Graph & Big Data Analytics — Applied Path, Buch 1 von 2. Buch 1 von 2 - Graph & Big Data Analytics — Applied Path
- Softcover
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Applied Graph Data Science : Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks and Applied Use Cases
Raj, Pethuru (EDT); Dutta, Pushan Kumar (EDT); Chong, Peter Han Joo (EDT); Song, Houbing Herbert (EDT); Zaitsev, Dmitry A. (EDT)
- Softcover
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Sprache: Englisch
Verlag: Elsevier Science & Technology, San Francisco 2025
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Paperback. Zustand: new. Paperback. Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its… practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Softcover
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Applied Graph Data Science : Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks and Applied Use Cases
Raj, Pethuru (EDT); Dutta, Pushan Kumar (EDT); Chong, Peter Han Joo (EDT); Song, Houbing Herbert (EDT); Zaitsev, Dmitry A. (EDT)
- Softcover
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Applied Graph Data Science : Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks and Applied Use Cases
Raj, Pethuru (EDT); Dutta, Pushan Kumar (EDT); Chong, Peter Han Joo (EDT); Song, Houbing Herbert (EDT); Zaitsev, Dmitry A. (EDT)
- Softcover
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Applied Graph Data Science : Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks and Applied Use Cases
Raj, Pethuru (EDT); Dutta, Pushan Kumar (EDT); Chong, Peter Han Joo (EDT); Song, Houbing Herbert (EDT); Zaitsev, Dmitry A. (EDT)
- Softcover
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes KönigreichGreatBookPricesUK
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Zustand: As New. Unread book in perfect condition.
- Softcover
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Paperback. Zustand: New. Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical…research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.
- Softcover
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Zustand: New. Provides comprehensive coverage of the emerging paradigm of graph data science and its real-world applicationsGives readers practical guidance on how to approach and solve complex data analysis problems using graph data science, with an emp.
- Softcover
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Paperback. Zustand: New. Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical…research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.
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Taschenbuch. Zustand: Neu. Neuware - Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with it…s practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.
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Paperback. Zustand: Brand New. 250 pages. 9.25x7.50x10.50 inches. In Stock. This item is printed on demand.
