Networks For Learning

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Networks for Learning

Author : Chris Brown
Publisher : Routledge
ISBN 13 : 1351996975
Total Pages : 220 pages
Book Rating : 4.3/5 (519 download)

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Book Synopsis Networks for Learning by : Chris Brown

Download or read book Networks for Learning written by Chris Brown and published by Routledge. This book was released on 2018-01-02 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learning Network Services for Professional Development

Author : Rob Koper
Publisher : Springer Science & Business Media
ISBN 13 : 3642009786
Total Pages : 411 pages
Book Rating : 4.6/5 (42 download)

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Book Synopsis Learning Network Services for Professional Development by : Rob Koper

Download or read book Learning Network Services for Professional Development written by Rob Koper and published by Springer Science & Business Media. This book was released on 2009-07-07 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: A "Learning Network" is a community of people who help each other to better understand and handle certain events and concepts in work or life. As a result – and sometimes also as an aim – participating in learning networks stimulates personal development, a better understanding of concepts and events, career development, and employability. "Learning Network Services" are Web services that are designed to facilitate the creation of distributed Learning Networks and to support the participants with various functions for knowledge exchange, social interaction, assessment and competence development in an effective way. The book presents state-of-the-art insights into the field of Learning Networks and Web-based services which can facilitate all kinds of processes within these networks.

Teaching Machines

Author : Audrey Watters
Publisher : MIT Press
ISBN 13 : 026254606X
Total Pages : 325 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Teaching Machines by : Audrey Watters

Download or read book Teaching Machines written by Audrey Watters and published by MIT Press. This book was released on 2023-02-07 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: How ed tech was born: Twentieth-century teaching machines--from Sidney Pressey's mechanized test-giver to B. F. Skinner's behaviorist bell-ringing box. Contrary to popular belief, ed tech did not begin with videos on the internet. The idea of technology that would allow students to "go at their own pace" did not originate in Silicon Valley. In Teaching Machines, education writer Audrey Watters offers a lively history of predigital educational technology, from Sidney Pressey's mechanized positive-reinforcement provider to B. F. Skinner's behaviorist bell-ringing box. Watters shows that these machines and the pedagogy that accompanied them sprang from ideas--bite-sized content, individualized instruction--that had legs and were later picked up by textbook publishers and early advocates for computerized learning. Watters pays particular attention to the role of the media--newspapers, magazines, television, and film--in shaping people's perceptions of teaching machines as well as the psychological theories underpinning them. She considers these machines in the context of education reform, the political reverberations of Sputnik, and the rise of the testing and textbook industries. She chronicles Skinner's attempts to bring his teaching machines to market, culminating in the famous behaviorist's efforts to launch Didak 101, the "pre-verbal" machine that taught spelling. (Alternate names proposed by Skinner include "Autodidak," "Instructomat," and "Autostructor.") Telling these somewhat cautionary tales, Watters challenges what she calls "the teleology of ed tech"--the idea that not only is computerized education inevitable, but technological progress is the sole driver of events.

Learning Networks

Author : Linda Marie Harasim
Publisher : MIT Press
ISBN 13 : 9780262082365
Total Pages : 366 pages
Book Rating : 4.0/5 (823 download)

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Book Synopsis Learning Networks by : Linda Marie Harasim

Download or read book Learning Networks written by Linda Marie Harasim and published by MIT Press. This book was released on 1995 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learning Together Online

Author : Starr Roxanne Hiltz
Publisher : Routledge
ISBN 13 : 1135615381
Total Pages : 320 pages
Book Rating : 4.1/5 (356 download)

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Book Synopsis Learning Together Online by : Starr Roxanne Hiltz

Download or read book Learning Together Online written by Starr Roxanne Hiltz and published by Routledge. This book was released on 2004-09-22 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

The Art of Learning

Author : Francis T.S. Yu
Publisher : CRC Press
ISBN 13 : 1351116320
Total Pages : 107 pages
Book Rating : 4.3/5 (511 download)

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Book Synopsis The Art of Learning by : Francis T.S. Yu

Download or read book The Art of Learning written by Francis T.S. Yu and published by CRC Press. This book was released on 2018-09-13 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the idea that innovative ways of teaching and learning are very essential to retention and growth. Presented in 15 sections, the book starts with the common sense training on education and moves on to neural network operation. Throughout the book, the art of learning, associative, cognitive, and creative learning are stated and defined. Learning simplicity, information content as related to neural network learning are discussed. The author also discusses neural plasticity and adaptability in smarter neural networks. If we know our human brain’s basic abilities and limitation then a better educational methods can be implemented. Presents the idea that innovative ways of teaching and learning are very essential to retention and growth Discusses major differences and constraints between neural network and computer Presents the significances of learning simplicity and information content as related to neural network learning are included Stresses the neural network learning capabilities and limitations and their role in developing more efficient learning techniques

Network Science In Education

Author : Catherine B. Cramer
Publisher : Springer
ISBN 13 : 3319772376
Total Pages : 205 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Network Science In Education by : Catherine B. Cramer

Download or read book Network Science In Education written by Catherine B. Cramer and published by Springer. This book was released on 2018-10-22 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Around the globe, there is an increasingly urgent need to provide opportunities for learners to embrace complexity; to develop the many skills and habits of mind that are relevant to today's complex and interconnected world; and to make learning more connected to our rapidly changing workplace and society. This presents an opportunity to (1) leverage new paradigms for understanding the structure and function of teaching and learning communities, and (2) to promote new approaches to developing methods, curricular materials, and resources. Network science - the study of connectivity - can play an important role in these activities, both as an important subject in teaching and learning and as a way to develop interconnected curricula. Since 2010, an international community of network science researchers and educators has come together to raise the global level of network literacy by applying ideas from network science to teaching and learning. Network Science in Education - which refers to both this community and to its activities - has evolved in response to the escalating activity in the field of network science and the need for people to be able to access the field through education channels. Network Science In Education: Transformational Approaches in Teaching and Learning appeals to both instructors and professionals, while offering case studies from a wide variety of activities that have been developed around the globe: the creation of entirely new courses and degree programs; tools for K-20 learners, teachers, and the general public; and in-depth analysis of selected programs. As network-based pedagogy and the community of practice continues to grow, we hope that the book's readers will join this vibrant network education community to build on these nascent ideas and help deepen the understanding of networks for all learners.

Machine Learning in Complex Networks

Author : Thiago Christiano Silva
Publisher : Springer
ISBN 13 : 3319172905
Total Pages : 331 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Machine Learning in Complex Networks by : Thiago Christiano Silva

Download or read book Machine Learning in Complex Networks written by Thiago Christiano Silva and published by Springer. This book was released on 2016-01-28 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas.

The Architecture of Productive Learning Networks

Author : Lucila Carvalho
Publisher : Routledge
ISBN 13 : 1135070180
Total Pages : 294 pages
Book Rating : 4.1/5 (35 download)

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Book Synopsis The Architecture of Productive Learning Networks by : Lucila Carvalho

Download or read book The Architecture of Productive Learning Networks written by Lucila Carvalho and published by Routledge. This book was released on 2014-03-14 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Architecture of Productive Learning Networks explores the characteristics of productive networked learning situations and, through a series of case studies, identifies some of the key qualities of successful designs. The case studies include networks from a variety of disciplinary and professional fields, including graphic design, chemistry, health care, library science, and teacher education. These learning networks have been implemented in a variety of settings: undergraduate courses in higher education, continuing professional development, and informal networks for creating and sharing knowledge on a particular topic. They are rich in reusable design ideas. The book introduces a framework for analyzing learning networks to show how knowledge, human interaction and physical and digital resources combine in the operation of productive learning networks. The book also argues that learning through interaction in networks has a long history. It combines ideas from architecture, anthropology, archaeology, education, sociology and organizational theory to illustrate and understand networked forms of learning.

Deep Learning

Author : Ian Goodfellow
Publisher : MIT Press
ISBN 13 : 0262337371
Total Pages : 801 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Deep Learning by : Ian Goodfellow

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Modular Learning in Neural Networks

Author : Tomas Hrycej
Publisher : Wiley-Interscience
ISBN 13 :
Total Pages : 264 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Modular Learning in Neural Networks by : Tomas Hrycej

Download or read book Modular Learning in Neural Networks written by Tomas Hrycej and published by Wiley-Interscience. This book was released on 1992-10-09 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Modular Learning in Neural Networks covers the full range of conceivable approaches to the modularization of learning, including decomposition of learning into modules using supervised and unsupervised learning types; decomposition of the function to be mapped into linear and nonlinear parts; decomposition of the neural network to minimize harmful interferences between a large number of network parameters during learning; decomposition of the application task into subtasks that are learned separately; decomposition into a knowledge-based part and a learning part. The book attempts to show that modular learning based on these approaches is helpful in improving the learning performance of neural networks. It demonstrates this by applying modular methods to a pair of benchmark cases - a medical classification problem of realistic size, encompassing 7,200 cases of thyroid disorder; and a handwritten digits classification problem, involving several thousand cases. In so doing, the book shows that some of the proposed methods lead to substantial improvements in solution quality and learning speed, as well as enhanced robustness with regard to learning control parameters.".

Strategic Networks

Author : Michael Gibbert
Publisher : John Wiley & Sons
ISBN 13 : 1405173335
Total Pages : 216 pages
Book Rating : 4.4/5 (51 download)

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Book Synopsis Strategic Networks by : Michael Gibbert

Download or read book Strategic Networks written by Michael Gibbert and published by John Wiley & Sons. This book was released on 2009-02-04 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Neural Networks and Deep Learning

Author : Charu C. Aggarwal
Publisher : Springer
ISBN 13 : 3319944630
Total Pages : 497 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Neural Networks and Deep Learning by : Charu C. Aggarwal

Download or read book Neural Networks and Deep Learning written by Charu C. Aggarwal and published by Springer. This book was released on 2018-08-25 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Neural Networks and Statistical Learning

Author : Ke-Lin Du
Publisher : Springer Science & Business Media
ISBN 13 : 1447155718
Total Pages : 824 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Neural Networks and Statistical Learning by : Ke-Lin Du

Download or read book Neural Networks and Statistical Learning written by Ke-Lin Du and published by Springer Science & Business Media. This book was released on 2013-12-09 with total page 824 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.

Learning Bayesian Networks

Author : Richard E. Neapolitan
Publisher : Prentice Hall
ISBN 13 :
Total Pages : 704 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis Learning Bayesian Networks by : Richard E. Neapolitan

Download or read book Learning Bayesian Networks written by Richard E. Neapolitan and published by Prentice Hall. This book was released on 2004 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

The Virtual Classroom

Author : Starr Roxanne Hiltz
Publisher : Intellect (UK)
ISBN 13 : 9781567500554
Total Pages : 412 pages
Book Rating : 4.5/5 (5 download)

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Book Synopsis The Virtual Classroom by : Starr Roxanne Hiltz

Download or read book The Virtual Classroom written by Starr Roxanne Hiltz and published by Intellect (UK). This book was released on 1994 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt:

The Principles of Deep Learning Theory

Author : Daniel A. Roberts
Publisher : Cambridge University Press
ISBN 13 : 1316519333
Total Pages : 473 pages
Book Rating : 4.3/5 (165 download)

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Book Synopsis The Principles of Deep Learning Theory by : Daniel A. Roberts

Download or read book The Principles of Deep Learning Theory written by Daniel A. Roberts and published by Cambridge University Press. This book was released on 2022-05-26 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume develops an effective theory approach to understanding deep neural networks of practical relevance.