大葉大學圖書館 |

Deep learning on graphs[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	006.31
書名/作者:	Deep learning on graphs/ Yao Ma, Jiliang Tang.
作者:	Ma, Yao.
其他作者:	Tang, Jiliang.
出版者:	Cambridge : : Cambridge University Press,, 2021.
面頁冊數:	xviii, 320 p. : : ill., digital ;; 24 cm.
附註:	Title from publisher's bibliographic system (viewed on 07 Oct 2021).
標題:	Machine learning.
標題:	Graph algorithms.
ISBN:	9781108924184
ISBN:	9781108831741
摘要、提要註:	Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.
電子資源:	https://doi.org/10.1017/9781108924184

Deep learning on graphs[electronic resource] /
Ma, Yao.

Deep learning on graphs[electronic resource] /Yao Ma, Jiliang Tang. - Cambridge :Cambridge University Press,2021. - xviii, 320 p. :ill., digital ;24 cm.

Title from publisher's bibliographic system (viewed on 07 Oct 2021).

Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.

ISBN: 9781108924184Subjects--Topical Terms:

202931
Machine learning.

LC Class. No.: Q325.5 / .M3 2021

Dewey Class. No.: 006.31

Deep learning on graphs[electronic resource] /
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