Introduction To Neural Networks
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Introduction To Neural Networks
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Author : Jeannette Lawrence
language : en
Publisher:
Release Date : 1994
Introduction To Neural Networks written by Jeannette Lawrence and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Computers categories.
Introduction To Neural Networks With Java
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Author : Jeff Heaton
language : en
Publisher: Heaton Research, Inc.
Release Date : 2008
Introduction To Neural Networks With Java written by Jeff Heaton and has been published by Heaton Research, Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computers categories.
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.
Introduction To Artificial Neural Networks
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Author : Sivanandam S., Paulraj M
language : en
Publisher: Vikas Publishing House
Release Date : 2009-11-01
Introduction To Artificial Neural Networks written by Sivanandam S., Paulraj M and has been published by Vikas Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-11-01 with Computers categories.
This fundamental book on Artificial Neural Networks has its emphasis on clear concepts, ease of understanding and simple examples. Written for undergraduate students, the book presents a large variety of standard neural networks with architecture, algorithms and applications.
An Introduction To Neural Networks
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Author : Kevin Gurney
language : en
Publisher: CRC Press
Release Date : 1997-08-05
An Introduction To Neural Networks written by Kevin Gurney and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-08-05 with Computers categories.
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.
Neural Networks
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Author : Raul Rojas
language : en
Publisher: Springer Science & Business Media
Release Date : 1996-07-12
Neural Networks written by Raul Rojas and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-07-12 with Computers categories.
Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.
Introduction To Deep Learning And Neural Networks With Pythontm
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Author : Ahmed Fawzy Gad
language : en
Publisher: Academic Press
Release Date : 2020-11-25
Introduction To Deep Learning And Neural Networks With Pythontm written by Ahmed Fawzy Gad and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Medical categories.
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
Introduction To Neural Network Verification
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Author : Aws Albarghouthi
language : en
Publisher:
Release Date : 2021-12-02
Introduction To Neural Network Verification written by Aws Albarghouthi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-02 with categories.
Over the past decade, a number of hardware and software advances have conspired to thrust deep learning and neural networks to the forefront of computing. Deep learning has created a qualitative shift in our conception of what software is and what it can do: Every day we're seeing new applications of deep learning, from healthcare to art, and it feels like we're only scratching the surface of a universe of new possibilities. This book offers the first introduction of foundational ideas from automated verification as applied to deep neural networks and deep learning. It is divided into three parts: Part 1 defines neural networks as data-flow graphs of operators over real-valued inputs. Part 2 discusses constraint-based techniques for verification. Part 3 discusses abstraction-based techniques for verification. The book is a self-contained treatment of a topic that sits at the intersection of machine learning and formal verification. It can serve as an introduction to the field for first-year graduate students or senior undergraduates, even if they have not been exposed to deep learning or verification.
An Introduction To Neural Networks
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Author : James A. Anderson
language : en
Publisher: MIT Press
Release Date : 1995
An Introduction To Neural Networks written by James A. Anderson and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.
An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind the assumptions of the models, as well as on what the models might be used for. It describes the mathematical and computational tools needed and provides an account of the author's own ideas. Students learn how to teach arithmetic to a neural network and get a short course on linear associative memory and adaptive maps. They are introduced to the author's brain-state-in-a-box (BSB) model and are provided with some of the neurobiological background necessary for a firm grasp of the general subject. The field now known as neural networks has split in recent years into two major groups, mirrored in the texts that are currently available: the engineers who are primarily interested in practical applications of the new adaptive, parallel computing technology, and the cognitive scientists and neuroscientists who are interested in scientific applications. As the gap between these two groups widens, Anderson notes that the academics have tended to drift off into irrelevant, often excessively abstract research while the engineers have lost contact with the source of ideas in the field. Neuroscience, he points out, provides a rich and valuable source of ideas about data representation and setting up the data representation is the major part of neural network programming. Both cognitive science and neuroscience give insights into how this can be done effectively: cognitive science suggests what to compute and neuroscience suggests how to compute it.
An Introduction To Neural Networks
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Author :
language : en
Publisher:
Release Date : 1995
An Introduction To Neural Networks written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with categories.
Artificial Neural Networks
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Author : Kevin L. Priddy
language : en
Publisher: SPIE Press
Release Date : 2005
Artificial Neural Networks written by Kevin L. Priddy and has been published by SPIE Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.
This tutorial text provides the reader with an understanding of artificial neural networks (ANNs), and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed, and the data collection processes, to the many ways ANNs are being used today. The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach the engineer the guiding principles necessary to use and apply artificial neural networks.