How we did it:
For any feedback, any questions, any notes or just for chat - feel free to follow us on social networks
Stuart Jonathan Russell, Peter Norvig
Artificial intelligence: A Modern Approach, 3e,is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. It is also a valuable resource for computer professionals, linguists, and cognitive scientists interested in artificial intelligence. The revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.
Christopher M. Bishop
The field of pattern recognition has undergone substantial development over the years. This book reflects these developments while providing a grounding in the basic concepts of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners.
Richard O. Duda, Peter E. Hart, David G. Stork
This edition has been completely revised, enlarged and formatted in two colour. It is a systematic account of the major topics in pattern recognition, based on the fundamental principles. It includes extensive examples, exercises and a solutions manual.
Richard S. Sutton, Andrew G. Barto
An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field's intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.
David B. Fogel
"Blondie24 tells the story of a computer that taught itself to play checkers far better than its creators ever could by emulating the principles of Darwinian evolution and discovering innovative ways to approach the game. In this year of 2001, as we remember Arthur C. Clarke's predictions, David Fogel dramatically demonstrates how evolutionary computation may enable humans to create a thinking machine far more readily than the techniques traditionally used in the study of artificial intelligence."--BOOK JACKET.
Learn how AI experts create intelligent game objects and characters with this first volume in the AI Game Programming Wisdom series. This unique collection of articles gives programmers and developers access to the insights and wisdom of over thirty AI pros. Each article delves deep into key AI game programming issues and provides insightful new ideas and techniques that can be easily integrated into your own games. Everything from general AI architectures, rule based systems, level-of-detail AI, scripting language issues, to expert systems, fuzzy logic, neural networks, and genetic algorithms are thoroughly covered. If you're a game programmer (AI/logic, front-end, user interface, tools, graphics, etc.) this comprehensive resource will help you take your skills and knowledge to the next level.
Laurene V. Fausett
Providing detailed examples of simple applications, this new book introduces the use of neural networks. It covers simple neural nets for pattern classification; pattern association; neural networks based on competition; adaptive-resonance theory; and more. For professionals working with neural networks.
Lawrence R. Rabiner, Biing-Hwang Juang
Provides a theoretically sound, technically accurate, and complete description of the basic knowledge and ideas that constitute a modern system for speech recognition by machine. Covers production, perception, and acoustic-phonetic characterization of the speech signal; signal processing and analysis methods for speech recognition; pattern comparison techniques; speech recognition system design and implementation; theory and implementation of hidden Markov models; speech recognition based on connected word models; large vocabulary continuous speech recognition; and task- oriented application of automatic speech recognition. For practicing engineers, scientists, linguists, and programmers interested in speech recognition.
Simon S. Haykin
For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Neural Networks and Learning Machines, Third Edition is 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. This is ideal for professional engineers and research scientists. Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/ Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.
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.
The aim of this work is to cover the basic concepts, with the key neural network models explored sufficiently deeply to allow a competent programmer to implement the networks in a language of their choice. The book is supported by a website.
This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assumed and all models are presented from the ground up. The principle focus of the book is the three layer feedforward network, for more than a decade as the workhorse of professional arsenals. Other network models with strong performance records are also included. Bound in the book is an IBM diskette that includes the source code for all programs in the book. Much of this code can be easily adapted to C compilers. In addition, the operation of all programs is thoroughly discussed both in the text and in the comments within the code to facilitate translation to other languages.
Igor Aleksander, Helen Morton
The second edition of this text has been updated and includes material on new developments including neurocontrol, pattern analysis and dynamic systems. The book should be useful for undergraduate students of neural networks.
Marcus Liwicki, Horst Bunke
This book addresses the task of processing online handwritten notes acquired from an electronic whiteboard, which is a new modality in handwriting recognition research. The main motivation of this book is smart meeting rooms, aim to automate standard tasks usually performed by humans in a meeting. The book can be summarized as follows. A new online handwritten database is compiled, and four handwriting recognition systems are developed. Moreover, novel preprocessing and normalization strategies are designed especially for whiteboard notes and a new neural network based recognizer is applied. Commercial recognition systems are included in a multiple classifier system. The experimental results on the test set show a highly significant improvement of the recognition performance to more than 86%.
Simon S. Haykin
Introduction; Learning processes; Single layer perceptrons; Multilayer perceptrons; Radial-basis function networks; Support vector machines; Comittee machines; Principal components analysis; Self-organizing maps; Information-theoretic models; Stochastic machines and their approximates rooted in statistical mechanics; neurodynamic programming; Temporal processing using feedforward networks; Neurodynamics; Dynamically driven recurrent networks; Epilogue; Bibliography; Index.
Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.
The areas covered here are those which are commonly managed by the generalist. The four contributions discuss: the autopsy in fatal non- missile head injuries; viral encephalitis and its pathology; a general approach to neuropathological problems; and dementia in middle and late life. Gives an overview of the network theory, including background review, basic concepts, associative networks, mapping networks, spatiotemporal networks, and adaptive resonance networks. Explores the principles of fuzzy logic. Annotation copyrighted by Book News, Inc., Portland, OR
This entertaining book is designed for the reader who enjoys thinking about new technologies and how to use them in solving practical problems. It provides reusable software modules for specific applications, as well as the methodology and spirit required to master problems for which there is no obvious solution. This book is for AI novices who want to learn new technologies and increase their capabilities and for AI professionals who want reusable application-oriented software modules to use in building their own systems. Each chapter contains background information and theory, a discussion of sample programs, program listings and output, additional information on the sample programs, and suggested exercises. Chapters use engaging real-world examples such as speech and handwriting recognition using neural networks, natural language processing with an example database interface, expert system shells, computer chess game, chaos theory, and fractal generation programs. The text assumes a reading knowledge of LISP and the implementation ability of a set of graphics primitives used for simple graphics operations. While all examples are implemented in Common LISP, the examples are also portable to other LISP dialects. The neural network and fractal examples are also portable to other languages such as C and Pascal.
José C. Príncipe, Neil R. Euliano, W. Curt Lefebvre
Develop New Insight into the Behavior of Adaptive Systems This one-of-a-kind interactive book and CD-ROM will help you develop a better understanding of the behavior of adaptive systems. Developed as part of a project aimed at innovating the teaching of adaptive systems in science and engineering, it unifies the concepts of neural networks and adaptive filters into a common framework. It begins by explaining the fundamentals of adaptive linear regression and builds on these concepts to explore pattern classification, function approximation, feature extraction, and time-series modeling/prediction. The text is integrated with the industry standard neural network/adaptive system simulator NeuroSolutions. This allows the authors to demonstrate and reinforce key concepts using over 200 interactive examples. Each of these examples is 'live,' allowing the user to change parameters and experiment first-hand with real-world adaptive systems. This creates a powerful environment for learning through both visualization and experimentation. Key Features of the Text * The text and CD combine to become an interactive learning tool. * Emphasis is on understanding the behavior of adaptive systems rather than mathematical derivations. * Each key concept is followed by an interactive example. * Over 200 fully functional simulations of adaptive systems are included. * The text and CD offer a unified view of neural networks, adaptive filters, pattern recognition, and support vector machines. * Hyperlinks allow instant access to keyword definitions, bibliographic references, equations, and advanced discussions of concepts. The CD-ROM Contains: * A complete, electronic version of the text in hypertext format * NeuroSolutions, an industry standard, icon-based neural network/adaptive system simulator * A tutorial on how to use NeuroSolutions * Additional data files to use with the simulator "An innovative approach to describing neurocomputing and adaptive learning systems from a perspective which unifies classical linear adaptive systems approaches with the modern advances in neural networks. It is rich in examples and practical insight." -James Zeidler, University of California, San Diego
Russell A. Poldrack, Jeanette A. Mumford, Thomas E. Nichols
Functional magnetic resonance imaging (fMRI) has become the most popular method for imaging brain function. Handbook for Functional MRI Data Analysis provides a comprehensive and practical introduction to the methods used for fMRI data analysis. Using minimal jargon, this book explains the concepts behind processing fMRI data, focusing on the techniques that are most commonly used in the field. This book provides background about the methods employed by common data analysis packages including FSL, SPM, and AFNI. Some of the newest cutting-edge techniques, including pattern classification analysis, connectivity modeling, and resting state network analysis, are also discussed. Readers of this book, whether newcomers to the field or experienced researchers, will obtain a deep and effective knowledge of how to employ fMRI analysis to ask scientific questions and become more sophisticated users of fMRI analysis software.
Wulfram Gerstner, Werner M. Kistler
This is an introduction to spiking neurons for advanced undergraduate or graduate students. It can be used with courses in computational neuroscience, theoretical biology, neural modeling, biophysics, or neural networks. It focuses on phenomenological approaches rather than detailed models in order to provide the reader with a conceptual framework. No prior knowledge beyond undergraduate mathematics is necessary to follow the book. Thus it should appeal to students or researchers in physics, mathematics, or computer science interested in biology; moreover it will also be useful for biologists working in mathematical modeling.
R Beale, T Jackson, Tom Jackson
An explanation of the basic concepts of neural computation, this book is about the whole field of neural networks and covers the major approaches and their results. It aims to develop concepts and ideas from their simple basics through their formulation into power computational systems.
Verilog HDL is a language for digital design, just as C is a language for programming. This complete Verilog HDL reference progresses from the basic Verilog concepts to the most advanced concepts in digital design. KEY TOPICS: " Covers the gamut of Verilog HDL fundamentals, such as gate, RTL, and behavioral modeling, all the way to advanced concepts, such as timing simulation, switch level modeling, PLI, and logic synthesis. For Verilog HDL digital IC and system design professionals.