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Baoling Shan – författare

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4 produkter

  1. Baoling Shan, Xin Yuan, Wei Ni, Ren Ping Liu, Eryk Dutkiewicz - Graph Learning Techniques, Häftad. Tillgänglighet: Lägg i varukorg

    Graph Learning Techniques

    Av Baoling Shan, Xin Yuan m. fl.

    Häftad, 2025

    808 kr

    Lägg i varukorg

    This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation. It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, …

  2. Baoling Shan, Xin Yuan, Wei Ni, Ren Ping Liu, Eryk Dutkiewicz - Graph Learning Techniques, Inbunden. Tillgänglighet: Lägg i varukorg

    Graph Learning Techniques

    Av Baoling Shan, Xin Yuan m. fl.

    Inbunden, 2025

    1522 kr

    Lägg i varukorg

    This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation. It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, …

  3. Eryk Dutkiewicz, Ren Ping Liu, Wei Ni, Xin Yuan, Baoling Shan - Graph Learning Techniques, E-bok. Tillgänglighet: Lägg i varukorg

    Graph Learning Techniques

    Av Eryk Dutkiewicz, Ren Ping Liu m. fl.

    E-bok, 2025

    953 kr

    Lägg i varukorg

    This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation. It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, …

  4. Eryk Dutkiewicz, Ren Ping Liu, Wei Ni, Xin Yuan, Baoling Shan - Graph Learning Techniques, E-bok. Tillgänglighet: Lägg i varukorg

    Graph Learning Techniques

    Av Eryk Dutkiewicz, Ren Ping Liu m. fl.

    E-bok, 2025

    953 kr

    Lägg i varukorg

    This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation. It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, …