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How does a telecommunications company function when its right hand often doesn't know what its left hand is doing? How do rapidly expanding, interdisciplinary organizations hold together and perform their knowledge work? In this book, Clay Spinuzzi draws on two warring theories of work activity - activity theory and actor-network theory - to examine the networks of activity that make a telecommunications company work and thrive. In doing so, Spinuzzi calls a truce between the two theories, bringing them to the negotiating table to parley about work. Specifically, about net work: the coordinative work that connects, coordinates, and stabilizes polycontextual work activities. To develop this uneasy dialogue, Spinuzzi examines the texts, trades, and technologies at play at Telecorp, both historically and empirically. Drawing on both theories, Spinuzzi provides new insights into how net work actually works and how our theories and research methods can be extended to better understand it.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Knowledge Networks describes the role of networks in the knowledge economy, explains network structures and behaviors, walks the reader through the design and setup of knowledge network analyses, and offers a step by step methodology for conducting a knowledge network analysis.
This book presents more than four decades of research in international business at the Department of Business Studies, Uppsala University. Gradually, this research has been recognized as 'The Uppsala School'. The work in Uppsala over the years reflects a broad palette of issues and approaches.
This text focuses on contemporary cutting edge research concerning the increasing strategic importance of subsidiary networks to the multinational firm. It combines contributions from three major related areas of inquiry; the changing theoretical conception of networks and the structure of the multinational firm, the importance of spillovers and agglomeration economies related to multinational investments and the management of the flow of information and knowledge from headquarters to subsidiaries and vice versa.
Introducing the basic concepts in total program control of the intelligent agents and machines, Intelligent Internet Knowledge Networks explores the design and architecture of information systems that include and emphasize the interactive role of modern computer/communication systems and human beings. Here, you’ll discover specific network configurations that sense environments, presented through case studies of IT platforms, electrical governments, medical networks, and educational networks.
Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners. Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons. This book explains recent progress in research and the state-of-the-art development of Federated Learning (FL), from the initial conception of the field to first applications and commercial use. To obtain this broad and deep overview, leading researchers address the different perspectives of federated learning: the core machine learning perspective, privacy and security, distributed systems, and specific application domains. Readers learn about the challenges faced in each of these areas, how they are interconnected, and how they are solved by state-of-the-art methods. Following an overview on federated learning basics in the introduction, over the following 24 chapters, the reader will dive deeply into various topics. A first part addresses algorithmic questions of solving different machine learning tasks in a federated way, how to train efficiently, at scale, and fairly. Another part focuses on providing clarity on how to select privacy and security solutions in a way that can be tailored to specific use cases, while yet another considers the pragmatics of the systems where the federated learning process will run. The book also covers other important use cases for federated learning such as split learning and vertical federated learning. Finally, the book includes some chapters focusing on applying FL in real-world enterprise settings.
No matter the industry, the development of information technologies has transformed how information is distributed and used to predict trends. Collecting and identifying the most vital information, however, requires constant management and manipulation. Current Issues and Trends in Knowledge Management, Discovery, and Transfer is an essential reference source that discusses crucial practices for collaborating and distributing work as well as validating accrued knowledge from real-time data. Featuring research on topics such as dynamic knowledge, management systems, and sharing behavior, this book is ideally designed for academics, researchers, librarians, managing professionals, and students seeking coverage on knowledge acquisition and implementation across systems.
This book outlines how network technology can support, foster and enhance the Knowledge Management, Sharing and Development (KMSD) processes in professional environments through the activation of both formal and informal knowledge flows. Understanding how ICT can be made available to such flows in the knowledge society is a factor that cannot be disregarded and is confirmed by the increasing interest of companies in new forms of software-mediated social interaction. The latter factor is in relation both to the possibility of accelerating internal communication and problem solving processes, and/or in relation to dynamics of endogenous knowledge growth of human resources.The book will focus specifically on knowledge flow (KF) processes occurring within networked communities of professionals (NCP) and the associated virtual community environments (VCE) that foster horizontal dynamics in the management, sharing and development of fresh knowledge. Along this line a further key issue will concern the analysis and evaluation techniques of the impact of Network Technology use on both community KF and NCP performance. - The proposal of a taxonomy of Network Technology uses to support formal and informal knowledge flows - Analyses how Web 2.0 and Web 3.0 technology is deeply modifying the dynamics connected to KF and KM - Discusses dynamics underlying horizontal KF sharing processes within NCP
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.