Search Results for "introduction-to-neural-networks-for-c-2nd-edition"

Introduction to Neural Networks with Java

Introduction to Neural Networks with Java

  • Author: Jeff Heaton
  • Publisher: Heaton Research, Inc.
  • ISBN: 1604390085
  • Category: Computers
  • Page: 440
  • View: 7531
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Introduction to Neural Networks in Java, Second Edition, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures such as the feedforward, Hopfield, and Self Organizing Map networks are discussed. Training techniques such as Backpropagation, Genetic Algorithms and Simulated Annealing are also introduced. Practical examples are given for each neural network. Examples include the Traveling Salesman problem, handwriting recognition, financial prediction, game strategy, learning mathematical functions and special application to Internet bots. All Java source code can be downloaded online.

Principles Of Artificial Neural Networks (2nd Edition)

Principles Of Artificial Neural Networks (2nd Edition)

  • Author: Graupe Daniel
  • Publisher: World Scientific
  • ISBN: 9814475564
  • Category: Neural networks (Computer science)
  • Page: 320
  • View: 1729
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The book should serve as a text for a university graduate course or for an advanced undergraduate course on neural networks in engineering and computer science departments. It should also serve as a self-study course for engineers and computer scientists in the industry. Covering major neural network approaches and architectures with the theories, this text presents detailed case studies for each of the approaches, accompanied with complete computer codes and the corresponding computed results. The case studies are designed to allow easy comparison of network performance to illustrate strengths and weaknesses of the different networks.

Introduction to Neural Networks

Introduction to Neural Networks

2nd Edition

  • Author: Architecture Technology Corpor
  • Publisher: Elsevier
  • ISBN: 1483295303
  • Category: Computers
  • Page: 72
  • View: 1003
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Please note this is a Short Discount publication. Neural network technology has been a curiosity since the early days of computing. Research in the area went into a near dormant state for a number of years, but recently there has been a new increased interest in the subject. This has been due to a number of factors: interest in the military, apparent ease of implementation, and the ability of the technology to develop computers which are able to learn from experience. This report summarizes the topic, providing the reader with an overview of the field and its potential direction. Included is an introduction to the technology and its future directions, as well as a set of examples of possible applications and potential implementation technologies.

Handbook of Research on Ubiquitous Computing Technology for Real Time Enterprises

Handbook of Research on Ubiquitous Computing Technology for Real Time Enterprises

  • Author: Mhlh„user, Max,Gurevych, Iryna
  • Publisher: IGI Global
  • ISBN: 1599048353
  • Category: Computers
  • Page: 662
  • View: 6418
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"This book combines the fundamental methods, algorithms, and concepts of pervasive computing with current innovations and solutions to emerging challenges. It systemically covers such topics as network and application scalability, wireless network connectivity, adaptability and "context-aware" computing, information technology security and liability, and human-computer interaction"--Provided by publisher.

Intelligent Systems for Engineers and Scientists

Intelligent Systems for Engineers and Scientists

  • Author: Adrian A. Hopgood
  • Publisher: CRC Press
  • ISBN: 1466516178
  • Category: Computers
  • Page: 451
  • View: 6713
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The third edition of this bestseller examines the principles of artificial intelligence and their application to engineering and science, as well as techniques for developing intelligent systems to solve practical problems. Covering the full spectrum of intelligent systems techniques, it incorporates knowledge-based systems, computational intelligence, and their hybrids. Using clear and concise language, Intelligent Systems for Engineers and Scientists, Third Edition features updates and improvements throughout all chapters. It includes expanded and separated chapters on genetic algorithms and single-candidate optimization techniques, while the chapter on neural networks now covers spiking networks and a range of recurrent networks. The book also provides extended coverage of fuzzy logic, including type-2 and fuzzy control systems. Example programs using rules and uncertainty are presented in an industry-standard format, so that you can run them yourself. The first part of the book describes key techniques of artificial intelligence—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), frames, objects, agents, symbolic learning, case-based reasoning, genetic algorithms, optimization algorithms, neural networks, hybrids, and the Lisp and Prolog languages. The second part describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. The author provides sufficient detail to help you develop your own intelligent systems for real applications. Whether you are building intelligent systems or you simply want to know more about them, this book provides you with detailed and up-to-date guidance. Check out the significantly expanded set of free web-based resources that support the book at: http://www.adrianhopgood.com/aitoolkit/

Introduction to Neural Networks

Introduction to Neural Networks

  • Author: Phil Picton
  • Publisher: Palgrave
  • ISBN: N.A
  • Category: Neural computers
  • Page: 168
  • View: 9371
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This introduction to neural networks describes what they are, what they can do and how they do it. While some scientific background is assumed, the reader is not expected to have any prior knowledge of neural networks. These networks are explained and discussed by means of examples, with the intention that by the end of the book the reader will have good, overall, up-to-date knowledge of developments in the field.

An Introduction to Optimization

An Introduction to Optimization

  • Author: Edwin K. P. Chong,Stanislaw H. Zak
  • Publisher: John Wiley & Sons
  • ISBN: 1118515153
  • Category: Mathematics
  • Page: 640
  • View: 3696
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Praise for the Third Edition ". . . guides and leads the reader through the learning path . . . [e]xamples are stated very clearly and the results are presented with attention to detail." —MAA Reviews Fully updated to reflect new developments in the field, the Fourth Edition of Introduction to Optimization fills the need for accessible treatment of optimization theory and methods with an emphasis on engineering design. Basic definitions and notations are provided in addition to the related fundamental background for linear algebra, geometry, and calculus. This new edition explores the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. The authors also present an optimization perspective on global search methods and include discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm. Featuring an elementary introduction to artificial neural networks, convex optimization, and multi-objective optimization, the Fourth Edition also offers: A new chapter on integer programming Expanded coverage of one-dimensional methods Updated and expanded sections on linear matrix inequalities Numerous new exercises at the end of each chapter MATLAB exercises and drill problems to reinforce the discussed theory and algorithms Numerous diagrams and figures that complement the written presentation of key concepts MATLAB M-files for implementation of the discussed theory and algorithms (available via the book's website) Introduction to Optimization, Fourth Edition is an ideal textbook for courses on optimization theory and methods. In addition, the book is a useful reference for professionals in mathematics, operations research, electrical engineering, economics, statistics, and business.

An Introduction to Machine Learning

An Introduction to Machine Learning

  • Author: Miroslav Kubat
  • Publisher: Springer
  • ISBN: 3319200100
  • Category: Computers
  • Page: 291
  • View: 3144
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This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.

Artificial Intelligence with Python

Artificial Intelligence with Python

Your complete guide to building intelligent apps using Python 3.x, 2nd Edition

  • Author: Alberto Artasanchez,Prateek Joshi
  • Publisher: Packt Publishing Ltd
  • ISBN: 1839216077
  • Category: Computers
  • Page: 618
  • View: 535
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New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more. Key Features Completely updated and revised to Python 3.x New chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineering Learn more about deep learning algorithms, machine learning data pipelines, and chatbots Book Description Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques. What you will learn Understand what artificial intelligence, machine learning, and data science are Explore the most common artificial intelligence use cases Learn how to build a machine learning pipeline Assimilate the basics of feature selection and feature engineering Identify the differences between supervised and unsupervised learning Discover the most recent advances and tools offered for AI development in the cloud Develop automatic speech recognition systems and chatbots Apply AI algorithms to time series data Who this book is for The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.

Neural Nets and Chaotic Carriers

Neural Nets and Chaotic Carriers

  • Author: Peter Whittle
  • Publisher: World Scientific
  • ISBN: 1908977957
  • Category: Science
  • Page: 244
  • View: 9029
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Neural Nets and Chaotic Carriers develops rational principles for the design of associative memories, with a view to applying these principles to models with irregularly oscillatory operation so evident in biological neural systems, and necessitated by the meaninglessness of absolute signal levels. Design is based on the criterion that an associative memory must be able to cope with “fading data”, i.e., to form an inference from the data even as its memory of that data degrades. The resultant net shows striking biological parallels. When these principles are combined with the Freeman specification of a neural oscillator, some remarkable effects emerge. For example, the commonly-observed phenomenon of neuronal bursting appears, with gamma-range oscillation modulated by a low-frequency square-wave oscillation (the “escapement oscillation”). Bridging studies and new results of artificial and biological neural networks, the book has a strong research character. It is, on the other hand, accessible to non-specialists for its concise exposition on the basics. Contents:Opening and Themes:Introduction and AspirationsOptimal Statistical ProceduresLinear Links and Nonlinear Knots: The Basic Neural NetBifurcations and ChaosAssociative and Storage Memories:What is a Memory? The Hamming and Hopfield NetsCompound and ‘Spurious’ TracesPreserving Plasticity: A Bayesian ApproachThe Key Task: The Fixing of Fading Data. Conclusions IPerformance of the Probability-Maximising AlgorithmOther Memories — Other ConsiderationsOscillatory Operation and the Biological Model:Neuron Models and Neural MassesFreeman Oscillators — Solo and in ConcertAssociative Memories Incorporating the Freeman OscillatorOlfactory Comparisons. Conclusions IITransmission Delays Readership: Professionals and graduates in areas associated with artificial neural networks. Keywords:Neural Nets;Associative Memory;Freeman Oscillator;Neuronal Bursting;Olfactory System