1# Library to Find Best eBook for Read
Author : Inderjit S. Dhillon
Release : 2013
Genre : Computer science
Kind : eBook
Book Rating : 747/5 ( reviews)
Book Synopsis Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining by : Inderjit S. Dhillon
Download or read book Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining written by Inderjit S. Dhillon. This book was released on 2013. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Robert Grossman
Release : 2013-08-11
Genre :
Kind : eBook
Book Rating : 721/5 ( reviews)
Book Synopsis Kdd'13 by : Robert Grossman
Download or read book Kdd'13 written by Robert Grossman. This book was released on 2013-08-11. Available in PDF, EPUB and Kindle. Book excerpt: KDD'13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Aug 11, 2013-Aug 14, 2013 Chicago, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.
Author : Longbing Cao
Release : 2015
Genre : Computer science
Kind : eBook
Book Rating : 642/5 ( reviews)
Book Synopsis Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining by : Longbing Cao
Download or read book Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining written by Longbing Cao. This book was released on 2015. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Ron Kohavi
Release : 2020-04-02
Genre : Computers
Kind : eBook
Book Rating : 098/5 ( reviews)
Book Synopsis Trustworthy Online Controlled Experiments by : Ron Kohavi
Download or read book Trustworthy Online Controlled Experiments written by Ron Kohavi. This book was released on 2020-04-02. Available in PDF, EPUB and Kindle. Book excerpt: Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to • Use the scientific method to evaluate hypotheses using controlled experiments • Define key metrics and ideally an Overall Evaluation Criterion • Test for trustworthiness of the results and alert experimenters to violated assumptions • Build a scalable platform that lowers the marginal cost of experiments close to zero • Avoid pitfalls like carryover effects and Twyman's law • Understand how statistical issues play out in practice.
Author : Lingfei Wu
Release : 2022-01-03
Genre : Computers
Kind : eBook
Book Rating : 549/5 ( reviews)
Book Synopsis Graph Neural Networks: Foundations, Frontiers, and Applications by : Lingfei Wu
Download or read book Graph Neural Networks: Foundations, Frontiers, and Applications written by Lingfei Wu. This book was released on 2022-01-03. Available in PDF, EPUB and Kindle. Book excerpt: Deep 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 has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.
