Principles Of Artificial Neural Networks
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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.
Principles Of Artificial Neural Networks
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Author : Daniel Graupe
language : en
Publisher: World Scientific
Release Date : 2013
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 2013 with COMPUTERS categories.
Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond. This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition OCo all with their respective source codes. These case studies demonstrate to the readers in detail how such case studies are designed and executed and how their specific results are obtained. The book is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining."
Principles Of Artificial Neural Networks Basic Designs To Deep Learning 4th Edition
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Author : Graupe Daniel
language : en
Publisher: World Scientific
Release Date : 2019-03-15
Principles Of Artificial Neural Networks Basic Designs To Deep Learning 4th Edition written by Graupe Daniel and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-15 with Computers categories.
The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new chapters, updates the earlier edition by highlighting recent developments in Deep-Learning Neural Networks, which are the recent leading approaches to neural networks. Uniquely, the book also includes case studies of applications of neural networks — demonstrating how such case studies are designed, executed and how their results are obtained.The title is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.
Principles Of Artificial Neural Networks
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Author : Daniel Graupe
language : en
Publisher: World Scientific
Release Date : 2013-07-31
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 2013-07-31 with Computers categories.
Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond. This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition — all with their respective source codes. These case studies demonstrate to the readers in detail how such case studies are designed and executed and how their specific results are obtained. The book is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining. Contents:Introduction and Role of Artificial Neural NetworksFundamentals of Biological Neural NetworksBasic Principles of ANNs and Their Early StructuresThe PerceptronThe MadalineBack PropagationHopfield NetworksCounter PropagationLarge Scale Memory Storage and Retrieval (LAMSTAR) NetworkAdaptive Resonance TheoryThe Cognitron and the NeocognitronStatistical TrainingRecurrent (Time Cycling) Back Propagation Networks Readership: Graduate and advanced senior students in artificial intelligence, pattern recognition & image analysis, neural networks, computational economics and finance, and biomedical engineering. Keywords:Neural Networks;Mathematical Derivations;Source Codes;Medical Applications;Data Mining;Cell-Shape Recognition;Micro-Trading
Principles Of Artificial Neural Networks
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Author : Daniel Graupe
language : en
Publisher: World Scientific
Release Date : 1997-05-01
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 1997-05-01 with Mathematics categories.
This textbook is intended for a first-year graduate course on Artificial Neural Networks. It assumes no prior background in the subject and is directed to MS students in electrical engineering, computer science and related fields, with background in at least one programming language or in a programming tool such as Matlab, and who have taken the basic undergraduate classes in systems or in signal processing.
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 Artificial intelligence 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.
Principles Of Artificial Neural Networks
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Author : Daniel Graupe
language : en
Publisher:
Release Date : 2019
Principles Of Artificial Neural Networks written by Daniel Graupe and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with COMPUTERS categories.
Designs And Applied Principles Of Artificial Neural Networks
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Author : Jeremy Rogerson
language : en
Publisher:
Release Date : 2015-03-23
Designs And Applied Principles Of Artificial Neural Networks written by Jeremy Rogerson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-23 with Neural networks (Computer science) categories.
This book presents a broad overview on the current various design modules and applications of artificial neural systems. Artificial neural networks are often dubbed as one of the most acknowledged technologies in the past twenty years. It has been broadly applied in a wide diversity of applications. The aim of this book is to present current developments of architectures, methodologies, and usage of artificial neural networks. The topics in this book majorly deal with two important aspects: the architecture aspect involving dealing with architectures, design, optimization, and the analysis of artificial neural networks; and the application aspect dealing with applications of artificial neural networks in a broad spectrum of areas comprising of biomedical, industrial, physics, and financial applications. This book will be an important source of reference for graduate students and engineers in various organizations.
Principles Of Neurocomputing For Science And Engineering
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Author : Frederic M. Ham
language : en
Publisher:
Release Date : 2001
Principles Of Neurocomputing For Science And Engineering written by Frederic M. Ham and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Neural computers categories.
This exciting new text covers artificial neural networks, but more specifically, neurocomputing. Neurocomputing is concerned with processing information, which involves a learning process within an artificial neural network architecture. This neural architecture responds to inputs according to a defined learning rule and then the trained network can be used to perform certain tasks depending on the application. Neurocomputing can play an important role in solving certain problems such as pattern recognition, optimization, event classification, control and identification of nonlinear systems, and statistical analysis. "Principles of Neurocomputing for Science and Engineering," unlike other neural networks texts, is written specifically for scientists and engineers who want to apply neural networks to solve complex problems. For each neurocomputing concept, a solid mathematical foundation is presented along with illustrative examples to accompany that particular architecture and associated training algorithm. The book is primarily intended for graduate-level neural networks courses, but in some instances may be used at the undergraduate level. The book includes many detailed examples and an extensive set of end-of-chapter problems.
Neural Networks With R
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Author : Giuseppe Ciaburro
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-09-27
Neural Networks With R written by Giuseppe Ciaburro and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-27 with Computers categories.
Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.