Networks for Learning

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Publisher : Routledge
ISBN 13 : 1351996975
Total Pages : 286 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 286 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.

Teaching Machines

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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

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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.

Deep Learning

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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.

Neural Network Learning

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Publisher : Cambridge University Press
ISBN 13 : 052157353X
Total Pages : 405 pages
Book Rating : 4.5/5 (215 download)

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Book Synopsis Neural Network Learning by : Martin Anthony

Download or read book Neural Network Learning written by Martin Anthony and published by Cambridge University Press. This book was released on 1999-11-04 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work explores probabilistic models of supervised learning problems and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, the authors develop a model of classification by real-output networks, and demonstrate the usefulness of classification...

Neural Networks and Deep Learning

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Publisher : Springer
ISBN 13 : 3319944630
Total Pages : 512 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 512 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 Network Learning and Expert Systems

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Publisher : MIT Press
ISBN 13 : 9780262071451
Total Pages : 392 pages
Book Rating : 4.0/5 (714 download)

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Book Synopsis Neural Network Learning and Expert Systems by : Stephen I. Gallant

Download or read book Neural Network Learning and Expert Systems written by Stephen I. Gallant and published by MIT Press. This book was released on 1993 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: presents a unified and in-depth development of neural network learning algorithms and neural network expert systems

Learning Together Online

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Publisher : Routledge
ISBN 13 : 113561539X
Total Pages : 321 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 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the past and future of research on the effectiveness of "asynchronous learning networks" courses in which students and teachers learn together online via the Internet. An integrated theoretical framework called "Online Interaction Lea

The Origins of Higher Learning

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Publisher : Taylor & Francis
ISBN 13 : 1317543270
Total Pages : 211 pages
Book Rating : 4.3/5 (175 download)

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Book Synopsis The Origins of Higher Learning by : Roy Lowe

Download or read book The Origins of Higher Learning written by Roy Lowe and published by Taylor & Francis. This book was released on 2016-10-04 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Higher education has become a worldwide phenomenon where students now travel internationally to pursue courses and careers, not simply as a global enterprise, but as a network of worldwide interconnections. The Origins of Higher Learning: Knowledge networks and the early development of universities is an account of the first globalisation that has led us to this point, telling of how humankind first developed centres of higher learning across the vast landmass from the Atlantic to the China Sea. This book opens a much-needed debate on the origins of higher learning, exploring how, why and where humankind first began to take a sustained interest in questions that went beyond daily survival. Showing how these concerns became institutionalised and how knowledge came to be transferred from place to place, this book explores important aspects of the forerunners of globalisation. It is a narrative which covers much of Asia, North Africa and Europe, many parts of which were little known beyond their own boundaries. Spanning from the earliest civilisations to the end of the European Middle Ages, around 700 years ago, here the authors set out crucial findings for future research and investigation. This book shows how interconnections across continents are nothing new and that in reality, humankind has been interdependent for a much longer period than is widely recognised. It is a book which challenges existing accounts of the origins of higher learning in Europe and will be of interest to all those who wish to know more about the world of academia.

The Principles of Deep Learning Theory

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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.

Machine Learning in Complex Networks

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Publisher : Springer
ISBN 13 : 3319172905
Total Pages : 345 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 345 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.

Graph Representation Learning

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Publisher : Springer Nature
ISBN 13 : 3031015886
Total Pages : 141 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Graph Representation Learning by : William L. William L. Hamilton

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Educational Networking

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Publisher : Springer Nature
ISBN 13 : 3030299732
Total Pages : 405 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Educational Networking by : Alejandro Peña-Ayala

Download or read book Educational Networking written by Alejandro Peña-Ayala and published by Springer Nature. This book was released on 2019-11-08 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is related to the educational networking (EN) domain, an incipient but disrupting trend engaged in extending and improving formal and informal academic practices by means of the support given by online social networks (OSNs) and Web 2.0 technologies. With the aim of contributing to spread the knowledge and development of the arena, this volume introduces ten recent works, whose content meets the quality criteria of formal scientific labor that is worthy to be published according to following five categories: · Reviews: gather three overviews that focus on K-12 EN practice, mixed methods approaches using social network analysis for learning and education, and a broad landscape of the recent accomplished labor. · Conceptual: presents a work where a theoretical framework is proposed to overcome barriers that constrain the use of OSNs for educational purposes by means of a Platform Adoption Model. · Projects: inform a couple of initiatives, where one fosters groups and networks for teachers involved in distance education, and the other encourages students the author academic videos to improve motivation and engagement. · Approaches: offer three experiences related to: Wiki and Blog usage for assessment affairs, application of a method that encourages OSNs users to actively post and repost valuable information for the learning community, and the recreation of learning spaces in context–aware to boost EN. · Study: applies an own method to ranking Mexican universities based on maximal clique, giving as a result a series of complex visual networks that characterize the tides among diverse features that describe academic institutions practice. In resume, this volume offers a fresh reference of an emergent field that contributes to spreading and enhancing the provision of education in classrooms and online settings through social constructivism and collaboration policy. Thus, it is expected the published content encourages researchers, practitioners, professors, and postgraduate students to consider their future contribution to extent the scope and impact of EN in formal and informal teaching and learning endeavors.

Neural Networks and Statistical Learning

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Publisher : Springer Science & Business Media
ISBN 13 : 1447155718
Total Pages : 834 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 834 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 and Soft Computing

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Publisher : MIT Press
ISBN 13 : 9780262112550
Total Pages : 556 pages
Book Rating : 4.1/5 (125 download)

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Book Synopsis Learning and Soft Computing by : Vojislav Kecman

Download or read book Learning and Soft Computing written by Vojislav Kecman and published by MIT Press. This book was released on 2001 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.

Introduction to Deep Learning and Neural Networks with PythonTM

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Publisher : Academic Press
ISBN 13 : 0323909345
Total Pages : 302 pages
Book Rating : 4.3/5 (239 download)

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Book Synopsis Introduction to Deep Learning and Neural Networks with PythonTM by : Ahmed Fawzy Gad

Download or read book Introduction to Deep Learning and Neural Networks with PythonTM written by Ahmed Fawzy Gad and published by Academic Press. This book was released on 2020-11-25 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Deep Learning and Neural Networks with PythonTM: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonTM code examples to clarify neural network calculations, by book's end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonTM examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. - Examines the practical side of deep learning and neural networks - Provides a problem-based approach to building artificial neural networks using real data - Describes PythonTM functions and features for neuroscientists - Uses a careful tutorial approach to describe implementation of neural networks in PythonTM - Features math and code examples (via companion website) with helpful instructions for easy implementation

Modular Learning in Neural Networks

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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.".