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Networks of Knowledge by Janice Gross Stein,Joy Fitzgibbon,Richard Stren Pdf
Examines the 'knowledge network' whose primary mandate is to create and disseminate knowledge based on multidisciplinary research that is informed by problem-solving as well as theoretical agendas.
The field; Learning networks: an introduction; Networks for schools: exemplars and experiences; Networks for higher education, training, and informal learning: exemplares and experiences; The guide; Designs for learning networks; Getting started: the implementation process; Teaching online; Learning online; Problems in paradise: expect the best, prepare for the worst; The future; New directions; Network learning: a paradign for the twenty-first century; Epilogue: email from the future; Appendixes; Indice.
Networks for Learning by Chris Brown,Cindy L. Poortman Pdf
Educational researchers, policy-makers and practitioners are increasingly focusing their attention on Professional Learning Networks in order to facilitate teacher development and encourage school and school system improvement. However, despite the understanding that PLNs can contribute significantly to improving teaching practice and student achievement, there are key challenges regarding their use. These challenges include: ensuring PLNs can provide opportunities for generating and sharing knowledge within schools enabling teachers and professionals to direct their own development helping individuals change their practices through inquiry-led approaches facilitating partnerships which work across a variety of stakeholders In this new edited volume, Brown and Poortman evaluate these challenges from both a theoretical and practical approach. A multitude of perspectives from a team of international contributors covers: the importance of Professional Learning Networks the use of evidence within PLNs the impact of inter-school networks international cases of networks and communities the promotion and sustainability of PLNs Also featuring case studies and exemplars to contextualise sustainable learning networks, Networks For Learning is an accessible and thoroughly-researched book, which will be essential reading and a valuable resource for researchers, teachers and school leaders who are interested in developing professional learning networks.
Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised. *NEW-New chapters now cover such areas as: - Support vector machines. - Reinforcement learning/neurodynamic programming. - Dynamically driven recurrent networks. *NEW-End-of-chapter problems revised, improved and expanded in number. Detailed solutions manual to accompany the text. *Extensive, state-of-the-art coverage exposes students to the many facets of neural networks and helps them appreciate the technologys capabilities and potential applications. *Detailed analysis of back-propagation learning and multi-layer perceptrons. *Explores the intricacies of the learning process-an essential component for understanding neural networks. *Considers recurrent networks, such as Hopfield networks, Boltzmann machines, and meanfield theory machines, as well as modular networks, temporal processing, and neurodynamics. *Integrates computer experiments throughout, giving students the opportunity to see how neural networks are designed and perform in practice. *Reinforces key concepts w
Learning Together Online by Starr Roxanne Hiltz,Ricki Goldman Pdf
This book is about the past and future of research on the effectiveness of learning networks (also known as "e-learning" or "online learning" or "Web-based learning"). Learning networks are groups of people using computer technology, communicating and collaborating online to build knowledge together. Over the past decade there has been an explosion not only of online courses, but also of studies on them. In Learning Together Online: Research on Asynchronous Learning Networks, leading researchers in the field use an integrated theoretical framework, which they call "Online Interaction Learning Theory," to organize what past research shows and where future research is going. It models the variables and processes that are important in determining the relative effectiveness of online learners working to reach a deeper level of understanding by interacting with each other and with the texts under investigation. Now that there have been hundreds of studies and thousands of courses offered online, what does the empirical evidence show? This book addresses the question directly by presenting what is known from research results about how to design and teach courses effectively online, ranging from the organizational context and characteristics of students to learning theories and research design methods. It also provides a research agenda for the next decade. Learning Together Online: Research on Asynchronous Learning Networks is both a textbook for graduate students and a professional reference for faculty teaching online, researchers conducting studies, and graduate students taking courses about learning technologies who need to know the state of the art of research in the area of online learning.
Deep Learning Networks by Jayakumar Singaram,S. S. Iyengar,Azad M. Madni Pdf
This textbook presents multiple facets of design, development and deployment of deep learning networks for both students and industry practitioners. It introduces a deep learning tool set with deep learning concepts interwoven to enhance understanding. It also presents the design and technical aspects of programming along with a practical way to understand the relationships between programming and technology for a variety of applications. It offers a tutorial for the reader to learn wide-ranging conceptual modeling and programming tools that animate deep learning applications. The book is especially directed to students taking senior level undergraduate courses and to industry practitioners interested in learning about and applying deep learning methods to practical real-world problems.
Professional Learning Networks in Design-Based Research Interventions by Mei Kuin Lai,Stuart McNaughton Pdf
Mei Kuin Lai and Stuart McNaughton provide a theoretical and practical account of how PLNs focused on collaborative analysis of data can be integrated into design-based research interventions to improve practice and student learning outcomes.
Building and Connecting Learning Communities by Steven Katz,Lorna M. Earl,Sonia Ben Jaafar Pdf
Networked learning communities: A powerful school improvement strategy for school leaders! Drawing on their work with schools in North America and England, the authors demonstrate how linking professional learning communities across school or district boundaries creates networked learning communities (NLCs) that can share professional knowledge. Through a sample school narrative, the book illustrates how NLCs can significantly improve instruction, increase student performance, and promote deep and sustained change. This resource examines: Collaborative inquiry as a process that challenges teachers’ thinking, generates new learning, and fosters trusting relationships Formal and informal leadership roles in NLCs How NLCs support systematic data analysis and accountability
Communities of Learning by C. J. Mews,Constant J. Mews,John N. Crossley Pdf
Communities of Learning: Networks and the Shaping of Intellectual Identity in Europe, 1100-1500 explores the fundamental insight that all new ideas are developed in the context of a community, whether academic, religious, or simply as a network of friends. The essays in this volume consider this notion in a variety of contexts and locations within Europe, from the pioneering age of translation activity in twelfth-century Toledo, when Jewish, Christian, and Muslim scholars came together to discuss Aristotle, to the origins of the University of Paris in the thirteenth century, and up to the period of great cultural renewal in France, Germany, and Italy in the fourteenth and fifteenth centuries. The collected essays bring together disciplinary approaches that are often discussed quite separately, namely that of the history of ideas, and the sociologies of both intellectual and religious life, with a view to exploring the multiplicity of communities in which ideas are pursued. Underpinning these various essays is an awareness of the delicate relationship between education and the diversity of religious practice and expression within Europe from 1100 to 1500. The collection emphasizes the fundamental continuity of intellectual concerns, which were shaped by both classical thought and monotheist religious tradition, but interpreted in a variety of ways.
Learning Bayesian Networks by Richard E. Neapolitan Pdf
In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.
Author : William L. William L. Hamilton Publisher : Springer Nature Page : 141 pages File Size : 51,7 Mb Release : 2022-06-01 Category : Computers ISBN : 9783031015885
Graph Representation Learning by William L. William L. Hamilton Pdf
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.
Strategic Networks by Michael Gibbert,Thomas Durand Pdf
This book explores the creation of 'learning networks' and shedslight on how they function:- real versus virtual forms ofinteraction, collaboration versus competition in the learningprocess, and joint value creation versus individual valueappropriation in networks. Written by international experts in the field of globalstrategy. Contributions have been selected for their insights andinterdependence between organizational learning and networks. Looks at topics such as real versus virtual forms ofinteraction, collaboration versus competition in the learningprocess, and joint value creation versus individual valueappropriation in networks.
Deep Learning by Ian Goodfellow,Yoshua Bengio,Aaron Courville Pdf
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.