Fundamentals of Artificial Neural Networks

Fundamentals of Artificial Neural Networks
Author :
Publisher : MIT Press
Total Pages : 546
Release :
ISBN-10 : 026208239X
ISBN-13 : 9780262082396
Rating : 4/5 (9X Downloads)

Synopsis Fundamentals of Artificial Neural Networks by : Mohamad H. Hassoun

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
Author :
Publisher : Springer Nature
Total Pages : 707
Release :
ISBN-10 : 9783030890100
ISBN-13 : 3030890104
Rating : 4/5 (00 Downloads)

Synopsis Multivariate Statistical Machine Learning Methods for Genomic Prediction by : Osval Antonio Montesinos López

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.

Introduction to Artificial Neural Networks

Introduction to Artificial Neural Networks
Author :
Publisher : Vikas Publishing House
Total Pages : 236
Release :
ISBN-10 : 9788125914259
ISBN-13 : 8125914250
Rating : 4/5 (59 Downloads)

Synopsis Introduction to Artificial Neural Networks by : Sivanandam S., Paulraj M

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.

Artificial Neural Networks

Artificial Neural Networks
Author :
Publisher : SPIE Press
Total Pages : 184
Release :
ISBN-10 : 0819459879
ISBN-13 : 9780819459879
Rating : 4/5 (79 Downloads)

Synopsis Artificial Neural Networks by : Kevin L. Priddy

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.

An Introduction to Neural Networks

An Introduction to Neural Networks
Author :
Publisher : CRC Press
Total Pages : 234
Release :
ISBN-10 : 9781482286991
ISBN-13 : 1482286998
Rating : 4/5 (91 Downloads)

Synopsis An Introduction to Neural Networks by : Kevin Gurney

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.

Fundamentals of Neural Networks

Fundamentals of Neural Networks
Author :
Publisher : Prentice Hall
Total Pages : 300
Release :
ISBN-10 : 013336769X
ISBN-13 : 9780133367690
Rating : 4/5 (9X Downloads)

Synopsis Fundamentals of Neural Networks by : Fausett

Elements of Artificial Neural Networks

Elements of Artificial Neural Networks
Author :
Publisher : MIT Press
Total Pages : 376
Release :
ISBN-10 : 0262133288
ISBN-13 : 9780262133289
Rating : 4/5 (88 Downloads)

Synopsis Elements of Artificial Neural Networks by : Kishan Mehrotra

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.

Principles Of Artificial Neural Networks (2nd Edition)

Principles Of Artificial Neural Networks (2nd Edition)
Author :
Publisher : World Scientific
Total Pages : 320
Release :
ISBN-10 : 9789814475563
ISBN-13 : 9814475564
Rating : 4/5 (63 Downloads)

Synopsis Principles Of Artificial Neural Networks (2nd Edition) by : Daniel Graupe

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.

Neural Networks for Applied Sciences and Engineering

Neural Networks for Applied Sciences and Engineering
Author :
Publisher : CRC Press
Total Pages : 596
Release :
ISBN-10 : 9781420013061
ISBN-13 : 1420013068
Rating : 4/5 (61 Downloads)

Synopsis Neural Networks for Applied Sciences and Engineering by : Sandhya Samarasinghe

In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in