Описание: This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gr?bner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.
Автор: Heiko Hamann Название: Swarm Robotics: A Formal Approach ISBN: 3319892797 ISBN-13(EAN): 9783319892795 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides an introduction to Swarm Robotics, which is the application of methods from swarm intelligence to robotics. It goes on to present methods that allow readers to understand how to design large-scale robot systems by going through many example scenarios on topics such as aggregation, coordinated motion (flocking), task allocation, self-assembly, collective construction, and environmental monitoring. The author explains the methodology behind building multiple, simple robots and how the complexity emerges from the multiple interactions between these robots such that they are able to solve difficult tasks. The book can be used as a short textbook for specialized courses or as an introduction to Swarm Robotics for graduate students, researchers, and professionals who want a concise introduction to the field.
Автор: Newman, Mark E. J. (anatol Rapoport Distinguished Название: Networks 2e hardback ISBN: 0198805098 ISBN-13(EAN): 9780198805090 Издательство: Oxford Academ Рейтинг: Цена: 10930.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book brings together recent advances and presents a comprehensive picture of the scientific study of networks. It includes discussion of computer networks, social networks, biological networks, and others, and an introduction to the mathematics of network theory, including analysis techniques, computer algorithms, and network modeling.
Автор: Chih-Lin I, Guanding Yu, Shuangfeng Han, Geoffrey Название: Green and Software-defined Wireless Networks: From Theory to Practice ISBN: 1108417329 ISBN-13(EAN): 9781108417327 Издательство: Cambridge Academ Рейтинг: Цена: 14573.00 р. Наличие на складе: Поставка под заказ.
Описание: An expert treatment of the state-of-the-art in green and soft communications, covering theory, 5G physical layer design, network architecture, energy efficient resource management strategies, and applications of wireless big data and artificial intelligence to wireless network design. Ideal for graduate students, professionals and researchers.
Автор: Maria Cristina Paganoni Название: Framing Big Data: A Linguistic and Discursive Approach ISBN: 3030167879 ISBN-13(EAN): 9783030167875 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book addresses big data as a socio-technical construct with huge potential for innovation in key sectors such as healthcare, government and business.
Описание: Social media is said to radically change the way in which public communication takes place: information diffuses faster and can reach a large number of people, but what makes the process so novel is that online networks can empower people to compete with traditional broadcasters or public figures. This book critically interrogates the contemporary relevance of social networks as a set of economic, cultural and political enterprises and as a public sphere in which a variety of political and socio-cultural demands can be met. It examines policy, regulatory and socio-cultural issues arising from the transformation of communication to a multi-layered sphere of online and social networks. The central theme of the book is to address the following questions: Are online and social networks an unstoppable democratizing and mobilizing force? Is there a need for policy and intervention to ensure the development of comprehensive and inclusive social networking frameworks? Social media are viewed both as a tool that allows citizens to influence policymaking, and as an object of new policies and regulations, such as data retention, privacy and copyright laws, around which citizens are mobilizing.
Описание: Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.What You’ll Learn Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework Use best practices for debugging and validating deep learning models Deploy and integrate deep learning as a service into a larger software service or product Extend deep learning principles into other popular frameworks Who This Book Is For Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.
Описание: This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gr?bner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.
This book presents a number of guidelines that are particularly useful in the context of decisions related to system-approach-based modern traffic engineering for the development of transport networks. Including practical examples and describing decision-making support systems it provides valuable insights for those seeking solutions to contemporary transport system problems on a daily basis, such as professional working for local authorities involved in planning urban and regional traffic development strategies as well as representatives of business and industry directly involved in implementing traffic engineering solutions. The guidelines provided enable readers to address problems in a timely manner and simplify the choice of appropriate strategies (including those connected with the relation between pedestrians and vehicle traffic flows, IT development in freight transport, safety issues related to accidents in road tunnels, but also open areas, like roundabouts and crossings). Furthermore, since the book also examines new theoretical-model approaches (including the model of arrival time distribution forming in a dense vehicle flow, the methodological basis of modelling and optimization of transport processes in the interaction of railways and maritime transport, traffic flow surveys and measurements, transport behaviour patterns, human factors in traffic engineering, and road condition modelling), it also appeals to researches and scientists studying these problems.
This book features selected papers submitted to and presented at the 16th Scientific and Technical Conference Transport Systems Theory and Practice organized by the Department of Transport Systems and Traffic Engineering at the Faculty of Transport of the Silesian University of Technology. The conference was held on 16-18 September 2019 in Katowice (Poland), more details at www.TSTP.polsl.pl.
Автор: Zhang Название: Toward Deep Neural Networks ISBN: 1138387037 ISBN-13(EAN): 9781138387034 Издательство: Taylor&Francis Рейтинг: Цена: 19140.00 р. Наличие на складе: Поставка под заказ.
Описание: This book introduces deep neural networks, with a focus on the weights-and-structure determination (WASD) algorithm. Based on the authors` 20 years of research experience on neuronets, the book explores the models, algorithms, and applications of the WASD neuronet.
Автор: Goldberg Yoav Название: Neural Network Methods in Natural Language Processing ISBN: 1627052984 ISBN-13(EAN): 9781627052986 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 11504.00 р. Наличие на складе: Нет в наличии.
Описание: Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.
Common patterns of interactions are altered in the digital world and new patterns of communication have emerged, challenging previous notions of what communication actually is in the contemporary age. Online configurations of interaction, such as video chats, blogging, and social networking practices demand profound rethinking of the categories of linguistic analysis, given the blurring of traditional distinctions between oral and written discourse in digital texts. This volume reconsiders underlying linguistic and semiotic frameworks of analysis of spoken and written discourse in the light of the new paradigms of online communication, in keeping with a multimodal corpus linguistics theoretical framework.
Typical modes of online interaction encompass speech, writing, gesture, movement, gaze, and social distance. This is nothing new, but here Sindoni asserts that all these modes are integrated in unprecedented ways, enacting new interactional patterns and new systems of interpretation among web users. These "non verbal" modes have been sidelined by mainstream linguistics, whereas accounting for the complexity of new genres and making sense of their educational impact is high on this volume's agenda. Sindoni analyzes other new phenomena, ranging from the intimate sphere (i.e. video chats, personal blogs or journals on social networking websites) to the public arena (i.e. global-scale transmission of information and knowledge in public blogs or media-sharing communities), shedding light on the rapidly changing global web scenario.
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