Graph Neural Networks: Foundations, Frontiers, and Applications
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Graph Neural Networks: Foundations, Frontiers, and Applications\nby Lingfei Wu\nPaperback\nEnglish\n\nDelivery\nUsually delivered in 2\u20133 working days (dispatched today if ordered before 3pm).\n\nBrand new copy from BookCurl \u2013 the UK independent online bookseller.\n\n------------------------------\nBook details\n------------------------------\nAuthor: Lingfei Wu\nTitle: Graph Neural Networks: Foundations, Frontiers, and Applications\nFormat: Paperback\nLanguage: English\nTopic: Machine learning\nPublisher: Springer Verlag, Singapore\nPublication year: 2023\nEdition: 1st ed. 2022\nISBN-13: 9789811660566\nISBN-10: 9811660565\n\n------------------------------\nDescription\n------------------------------\n\nDeep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap ha.
Graph Neural Networks: Foundations, Frontiers, and Applications
Graph Neural Networks: Foundations, Frontiers, and Applications\nby Lingfei Wu\nPaperback\nEnglish\n\nDelivery\nUsually delivered in 2\u20133 working days (dispatched today if ordered before 3pm).\n\nBrand new copy from BookCurl \u2013 the UK independent online bookseller.\n\n------------------------------\nBook details\n------------------------------\nAuthor: Lingfei Wu\nTitle: Graph Neural Networks: Foundations, Frontiers, and Applications\nFormat: Paperback\nLanguage: English\nTopic: Machine learning\nPublisher: Springer Verlag, Singapore\nPublication year: 2023\nEdition: 1st ed. 2022\nISBN-13: 9789811660566\nISBN-10: 9811660565\n\n------------------------------\nDescription\n------------------------------\n\nDeep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap ha.
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