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On Intelligence
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by
Jeff Hawkins, Sandra Blakeslee
From the inventor of the PalmPilot comes a new and compelling theory of intelligence, brain function, and the future of intelligent machines. Written with acclaimed science writer Blakeslee, this text promises to transfigure the possibilities of the technology age.
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Business @ the Speed of Thought: Succeeding in the Digital Economy
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by
Bill Gates, Collins Hemingway
Bill Gates draws on his own experiences as chairman of Microsoft and offers his best insights into how companies can become successful. He advocates that companies develop what he calls "digital nervous systems"--a way of using computers to maximize the flow of information for profit, and provides examples from companies he thinks have done things ...
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Neural Networks for Pattern Recognition
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Christopher M Bishop
This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer ...
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C++ Neural Networks and Fuzzy Logic
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by
Valluru B Rao
This text teaches the core concepts of fuzzy logic and neural network technologies, including back propagation and adaptive resonance theory. It includes practical applications demonstrating pattern recognition, optimization and financial modelling. The disk includes all the source code discussed.
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Cellular Neural Networks and Visual Computing: Foundations and Applications
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by
Leon O Chua, Tamas Roska
Cellular Nonlinear/neural Network (CNN) technology is both a revolutionary concept and an experimentally proven new computing paradigm. Analogic cellular computers based on CNNs are set to change the way analog signals are processed and are paving the way to an entire new analog computing industry. This unique undergraduate level textbook includes ...
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Statistical Mechanics of Learning
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by
A Engel, C Van Den Broeck
Learning is one of the things that humans do naturally, and it has always been a challenge for us to understand the process. Nowadays this challenge has another dimension as we try to build machines that are able to learn and to undertake tasks such as datamining, image processing and pattern recognition. We can formulate a simple framework, ...
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Neural Networks in Finance: Gaining Predictive Edge in the Market
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by
Paul McNelis
This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from ...
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Artificial Life: A Report from the Frontier Where Computers Meet Biology
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by
Steven Levy
Even as molecular biologists attempt to reproduce life in vitro, another group of scientists is creating life--or something very close to it--in silico, using computers to produce "organisms" that can move, see, feed, reproduce, and die. Photos.
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Bayesian Networks and Decision Graphs
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by
Finn B Jensen
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query ...
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Fundamentals of Neural Networks: Architectures, Algorithms and Applications
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by
Laurene V Fausett
An exceptionally clear, thorough introduction to neural networks written at an elementary level. Written with the beginning student in mind, the text features systematic discussions of all major neural networks and fortifies the reader's understanding with many examples. An exceptionally clear, thorough introduction to neural networks written at ...
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Neural networks algorithms, applications, and programming techniques
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by
Freeman, Skapura
Freeman and Skapura provide a practical introduction to artificial neural systems (ANS). The authors survey the most common neural-network architectures and show how neural networks can be used to solve actual scientific and engineering problems and describe methodologies for simulating neural-network architectures on traditional digital computing ...
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Neural Networks and Learning Machines
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by
Simon Haykin
For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. 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. Refocused, revised ...
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Building Neural Networks
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David M Skapura, Peter S Gordon (Editor)
This practical introduction describes the kinds of real-world problems neural network technology can solve. Surveying a range of neural network applications, the book demonstrates the construction and operation of artificial neural systems. Through numerous examples, the author explains the process of building neural-network applications that ...
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Neural Network and Fuzzy Logic Applications in C/C++
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by
Stephen T Welstead
Using an engineering and science perspective, it explores diverse neural network, fuzzy logic and genetic algorithm techniques, plus developing applications best suited for each of the methods discussed. Sample results are described and judgment made as to how well each application worked. The book/disk set includes an object-oriented user ...
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Neural Networks in C++: An Object-Oriented Framework for Building Connectionist Systems
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by
Adam Blum
Following an introduction to neural networks, this text describes the basics of object-orientation for potential users. Sample applications are developed and demonstrated in a variety of areas, including image recognition, text processing and forecasting. Program listings for the these applications are provided in their entirety using Borland C++ ...
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Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence
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by
Bart Kosko, Ph.D.
Combining neural networks and fuzzy systems, this presents neural networks as trainable dynamical systems and develops mechanisms and principles of adaption, self-organization, covergence and global stability. It also includes the new geometric theory of fuzzy sets, systems and associative memories. Kosko shows how to apply fuzzy theory to ...
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Signal and Image Processing with Neural Networks: A C++ Sourcebook
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by
Timothy Masters
This book will guide the reader in learning practical, proven techniques for integrating traditional signal and image processing methods with modern neural networks. It provides thorough coverage of complex-domain neural networks, which are generally superior to the usual real domain models when processing complete data. The author presents a ...
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Cognizers: Neural Networks and Machines That Think
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by
R. Colin Johnson, Chappell Brown
One of the most dramatic developments in computer science has been the effort to create machines that duplicate the neurotransmitter biology of the human brain. Describing for the general reader how human neural networks work, the authors explain in this text how this new technology could be the breakthrough that makes artificial intelligence a ...
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Darwin Among the Machines: The Evolution of Global Intelligence
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by
George Dyson
Darwin Among the Machines retraces the steps that led us into the digital wilderness, no less wild for being a universe of our own device. Introducing a cast of known and unknown characters, George B. Dyson traces the course of the information revolution, illuminating the lives and work of visionaries-from the time of Thomas Hobbes to the time ...
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Neural Networks and Psychopathology: Connectionist Models in Practice and Research
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by
Dan J Stein, M.D. (Editor), Jacques Ludik (Editor)
Research on connectionist models is one of the most exciting areas in cognitive science, and neural network models of psychopathology have immediate theoretical and empirical appeal. The contributors to this study review theoretical, historical and clinical issues, including the contribution of neural network models to diagnosis, pharmacotherapy ...
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An Introduction to the Modeling of Neural Networks
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by
Peretto
This text is a beginning graduate-level introduction to neural networks, focussing on current theoretical models, examining what these models can reveal about how the brain functions, and discussing the ramifications for psychology, artificial intelligence and the construction of a new generation of intelligent computers. The book is divided into ...
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Enchanted Looms: Conscious Networks in Brains and Computers
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by
Rodney M J Cotterill
The title of this book was inspired by a passage in Charles Sherrington's Man on his Nature. When that famous physiologist died in 1952, the prospects for a scientific explanation of consciousness seemed remote. Enchanted Looms shows how the situation has changed dramatically, and provides what is probably the most wide-ranging account of the ...
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Imitation of Life: How Biology Is Inspiring Computing
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by
Nancy Forbes
As computers and the tasks they perform become increasingly complex, researchers are looking to nature - as model and as metaphor - for inspiration. The organisation and behaviour of biological organisms present scientists with an invitation to reinvent computing for the complex tasks of the future. In Imitation of Life Nancy Forbes surveys the ...
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Learning from Data: Concepts, Theory, and Methods
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by
Vladimir Cherkassky, Filip M Mulier
An interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic This book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic ...
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Information-Theoretic Aspects of Neural Networks
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by
Perambur S Neelakanta, Ph.D., C.Eng. (Preface by)
Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural ...
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