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Principles of Artificial Neural Networks

Principles of Artificial Neural Networks
Author: Daniel Graupe
Publisher: World Scientific
Total Pages: 382
Release: 2013
Genre: COMPUTERS
ISBN: 9814522740

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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

Principles of Artificial Neural Networks
Author: Daniel Graupe
Publisher: World Scientific
Total Pages: 500
Release: 2013-07-31
Genre: Computers
ISBN: 9814522759

Download Principles of Artificial Neural Networks Book in PDF, ePub and Kindle

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: Basic Designs To Deep Learning (4th Edition)

Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition)
Author: Graupe Daniel
Publisher: World Scientific
Total Pages: 440
Release: 2019-03-15
Genre: Computers
ISBN: 9811201242

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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

Principles of Artificial Neural Networks
Author: Daniel Graupe
Publisher: World Scientific
Total Pages: 256
Release: 1997-05-01
Genre: Mathematics
ISBN: 9789810241254

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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.


The Principles of Deep Learning Theory

The Principles of Deep Learning Theory
Author: Daniel A. Roberts
Publisher: Cambridge University Press
Total Pages: 473
Release: 2022-05-26
Genre: Computers
ISBN: 1316519333

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This volume develops an effective theory approach to understanding deep neural networks of practical relevance.


Neural Smithing

Neural Smithing
Author: Russell Reed
Publisher: MIT Press
Total Pages: 359
Release: 1999-02-17
Genre: Computers
ISBN: 0262181908

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Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research.


Multivariate Statistical Machine Learning Methods for Genomic Prediction

Multivariate Statistical Machine Learning Methods for Genomic Prediction
Author: Osval Antonio Montesinos López
Publisher: Springer Nature
Total Pages: 707
Release: 2022-02-14
Genre: Technology & Engineering
ISBN: 3030890104

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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.


Neural Networks with R

Neural Networks with R
Author: Giuseppe Ciaburro
Publisher: Packt Publishing Ltd
Total Pages: 270
Release: 2017-09-27
Genre: Computers
ISBN: 1788399412

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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.


Artificial Neural Networks in Biological and Environmental Analysis

Artificial Neural Networks in Biological and Environmental Analysis
Author: Grady Hanrahan
Publisher: CRC Press
Total Pages: 206
Release: 2011-01-18
Genre: Mathematics
ISBN: 1439812594

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Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound


Process Neural Networks

Process Neural Networks
Author: Xingui He
Publisher: Springer Science & Business Media
Total Pages: 240
Release: 2010-07-05
Genre: Computers
ISBN: 3540737626

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For the first time, this book sets forth the concept and model for a process neural network. You’ll discover how a process neural network expands the mapping relationship between the input and output of traditional neural networks and greatly enhances the expression capability of artificial neural networks. Detailed illustrations help you visualize information processing flow and the mapping relationship between inputs and outputs.