Download Full eBooks in PDF
Visual C Net Cheng Xu She Ji Jiao Cheng Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Visual C Net Cheng Xu She Ji Jiao Cheng book. This book definitely worth reading, it is an incredibly well-written.
Author : 朱志良,李丹程
Publisher : Unknown
Page : 389 pages
File Size : 44,5 Mb
Release : 2019
Category : BASIC (Computer program language)
ISBN : OCLC:1308887458
Author : 黄嘉辉
Publisher : Unknown
Page : 128 pages
File Size : 43,8 Mb
Release : 2003
Category : Electronic
ISBN : 7900122796
Author : 吴长海,陈达
Publisher : Unknown
Page : 128 pages
File Size : 43,9 Mb
Release : 2010
Category : Electronic
ISBN : 7030265017
Author : 杨学全
Publisher : Unknown
Page : 458 pages
File Size : 55,9 Mb
Release : 2012
Category : Electronic
ISBN : 7121148455
Author : 李雁翎,万玉
Publisher : Unknown
Page : 141 pages
File Size : 42,8 Mb
Release : 2012
Category : Electronic
ISBN : 7115269262
Author : 王明福
Publisher : Unknown
Page : 128 pages
File Size : 54,8 Mb
Release : 2010
Category : Electronic
ISBN : 7040290715
Author : Anonim
Publisher : Unknown
Page : 346 pages
File Size : 43,8 Mb
Release : 2015
Category : Electronic
ISBN : 704042794X
Author : Qiang Qiang Yang,Yang Yang Liu,Yong Yong Cheng,Yan Yan Kang,Tianjian Tianjian Chen,Han Han Yu
Publisher : Springer Nature
Page : 189 pages
File Size : 53,9 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015854
How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
Author : William L. William L. Hamilton
Publisher : Springer Nature
Page : 141 pages
File Size : 41,6 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015885
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Author : Shoji Makino,Jingdong Chen
Publisher : Springer Science & Business Media
Page : 432 pages
File Size : 52,8 Mb
Release : 2005-03-17
Category : Computers
ISBN : 354024039X
We live in a noisy world! In all applications (telecommunications, hands-free communications, recording, human-machine interfaces, etc) that require at least one microphone, the signal of interest is usually contaminated by noise and reverberation. As a result, the microphone signal has to be "cleaned" with digital signal processing tools before it is played out, transmitted, or stored. This book is about speech enhancement. Different well-known and state-of-the-art methods for noise reduction, with one or multiple microphones, are discussed. By speech enhancement, we mean not only noise reduction but also dereverberation and separation of independent signals. These topics are also covered in this book. However, the general emphasis is on noise reduction because of the large number of applications that can benefit from this technology. The goal of this book is to provide a strong reference for researchers, engineers, and graduate students who are interested in the problem of signal and speech enhancement. To do so, we invited well-known experts to contribute chapters covering the state of the art in this focused field.
Author : Yuehua Liu,Daozhong Yao
Publisher : Cheng & Tsui
Page : 335 pages
File Size : 54,8 Mb
Release : 2008-01-01
Category : Juvenile Nonfiction
ISBN : 088727644X
Cheng & Tsui's best-loved Chinese textbook series is new, revised, and better than ever!
Author : Zhiyuan Liu,Yankai Lin,Maosong Sun
Publisher : Springer Nature
Page : 319 pages
File Size : 50,6 Mb
Release : 2020-07-03
Category : Computers
ISBN : 9789811555732
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
Author : Qing Cao,Hailong Tian,Paul Chilton
Publisher : John Benjamins Publishing Company
Page : 213 pages
File Size : 46,8 Mb
Release : 2014-04-23
Category : Language Arts & Disciplines
ISBN : 9789027270368
After three and a half decades of economic reforms, radical changes have occurred in all aspects of life in China. In an authoritarian society, these changes are mediated significantly through the power of language, carefully controlled by the political elites. Discourse, as a way of speaking and doing things, has become an indispensable instrument for the authority to manage a fluid, increasingly fragmented, but highly dynamic and yet fragile society. Written by an international team of leading scholars, this volume examines socio-political transformations of contemporary Chinese society through a systematic account, analysis and assessment of its salient discourses and their production, circulation, negotiation, and consequences. In particular, the volume focuses on the interplay of politics and media. The book’s intended readership is academics and students of Chinese studies, language and discourse, and media and communication studies.
Author : Linda Waite,Maggie Gallagher
Publisher : Crown
Page : 274 pages
File Size : 42,9 Mb
Release : 2002-03-05
Category : Social Science
ISBN : 9780767910866
A groundbreaking look at marriage, one of the most basic and universal of all human institutions, which reveals the emotional, physical, economic, and sexual benefits that marriage brings to individuals and society as a whole. The Case for Marriage is a critically important intervention in the national debate about the future of family. Based on the authoritative research of family sociologist Linda J. Waite, journalist Maggie Gallagher, and a number of other scholars, this book’s findings dramatically contradict the anti-marriage myths that have become the common sense of most Americans. Today a broad consensus holds that marriage is a bad deal for women, that divorce is better for children when parents are unhappy, and that marriage is essentially a private choice, not a public institution. Waite and Gallagher flatly contradict these assumptions, arguing instead that by a broad range of indices, marriage is actually better for you than being single or divorced– physically, materially, and spiritually. They contend that married people live longer, have better health, earn more money, accumulate more wealth, feel more fulfillment in their lives, enjoy more satisfying sexual relationships, and have happier and more successful children than those who remain single, cohabit, or get divorced. The Case for Marriage combines clearheaded analysis, penetrating cultural criticism, and practical advice for strengthening the institution of marriage, and provides clear, essential guidelines for reestablishing marriage as the foundation for a healthy and happy society. “A compelling defense of a sacred union. The Case for Marriage is well written and well argued, empirically rigorous and learned, practical and commonsensical.” -- William J. Bennett, author of The Book of Virtues “Makes the absolutely critical point that marriage has been misrepresented and misunderstood.” -- The Wall Street Journal www.broadwaybooks.com
Author : Sarit Kraus
Publisher : Unknown
Page : 128 pages
File Size : 43,9 Mb
Release : 2019
Category : Electronic
ISBN : 0999241141