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Fundamentals Of Artificial Neural Networks


Fundamentals Of Artificial Neural Networks
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Fundamentals Of Artificial Neural Networks


Fundamentals Of Artificial Neural Networks
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Author : Mohamad H. Hassoun
language : en
Publisher: MIT Press
Release Date : 1995

Fundamentals Of Artificial Neural Networks written by Mohamad H. Hassoun 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.


A systematic account of artificial neural network paradigms that identifies fundamental concepts and major methodologies. Important results are integrated into the text in order to explain a wide range of existing empirical observations and commonly used heuristics.



Multivariate Statistical Machine Learning Methods For Genomic Prediction


Multivariate Statistical Machine Learning Methods For Genomic Prediction
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Author : Osval Antonio Montesinos López
language : en
Publisher: Springer Nature
Release Date : 2022-02-14

Multivariate Statistical Machine Learning Methods For Genomic Prediction written by Osval Antonio Montesinos López and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-14 with Technology & Engineering categories.


This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.



Fundamentals Of Neural Networks Architectures Algorithms And Applications


Fundamentals Of Neural Networks Architectures Algorithms And Applications
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Author : Laurene V. Fausett
language : en
Publisher: Pearson Education India
Release Date : 2006

Fundamentals Of Neural Networks Architectures Algorithms And Applications written by Laurene V. Fausett and has been published by Pearson Education India this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Neural networks (Computer science) categories.




Principles Of Artificial Neural Networks


Principles Of Artificial Neural Networks
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Author : Daniel Graupe
language : en
Publisher: World Scientific
Release Date : 2007

Principles Of Artificial Neural Networks written by Daniel Graupe and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.


This book should serves as a self-study course for engineers and computer scientist in the industry. The features include major neural network approaches and architectures with theories and detailed case studies for each of the approaches acompanied by complete computer codes and the corresponding computed results. There is also a chapter on LAMSTAR neural network.



Fundamentals Of Neural Networks


Fundamentals Of Neural Networks
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Author : Fausett
language : en
Publisher: Prentice Hall
Release Date : 1994

Fundamentals Of Neural Networks written by Fausett and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with categories.




Neural Networks In The Analysis And Design Of Structures


Neural Networks In The Analysis And Design Of Structures
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Author : Zenon Waszczysznk
language : en
Publisher: Springer Science & Business Media
Release Date : 1999

Neural Networks In The Analysis And Design Of Structures written by Zenon Waszczysznk 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 1999 with Computers categories.


Neural Networks are a new, interdisciplinary tool for information processing. Neurocomputing being successfully introduced to structural problems which are difficult or even impossible to be analysed by standard computers (hard computing). The book is devoted to foundations and applications of NNs in the structural mechanics and design of structures.



An Introduction To Neural Networks


An Introduction To Neural Networks
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Author : Kevin Gurney
language : en
Publisher: CRC Press
Release Date : 2018-10-08

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 2018-10-08 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 Network Fundamentals With Graphs Algorithms And Applications


Neural Network Fundamentals With Graphs Algorithms And Applications
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Author : Nirmal K. Bose
language : en
Publisher: McGraw-Hill Companies
Release Date : 1996

Neural Network Fundamentals With Graphs Algorithms And Applications written by Nirmal K. Bose and has been published by McGraw-Hill Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.




Elements Of Artificial Neural Networks


Elements Of Artificial Neural Networks
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Author : Kishan Mehrotra
language : en
Publisher: MIT Press
Release Date : 1997

Elements Of Artificial Neural Networks written by Kishan Mehrotra and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Computers categories.


Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have been developing and team teaching the material in a one-semester course over the past six years, describe most of the basic neural network models (with several detailed solved examples) and discuss the rationale and advantages of the models, as well as their limitations. The approach is practical and open-minded and requires very little mathematical or technical background. Written from a computer science and statistics point of view, the text stresses links to contiguous fields and can easily serve as a first course for students in economics and management. The opening chapter sets the stage, presenting the basic concepts in a clear and objective way and tackling important -- yet rarely addressed -- questions related to the use of neural networks in practical situations. Subsequent chapters on supervised learning (single layer and multilayer networks), unsupervised learning, and associative models are structured around classes of problems to which networks can be applied. Applications are discussed along with the algorithms. A separate chapter takes up optimization methods. The most frequently used algorithms, such as backpropagation, are introduced early on, right after perceptrons, so that these can form the basis for initiating course projects. Algorithms published as late as 1995 are also included. All of the algorithms are presented using block-structured pseudo-code, and exercises are provided throughout. Software implementing many commonly used neural network algorithms is available at the book's website. Transparency masters, including abbreviated text and figures for the entire book, are available for instructors using the text.