Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition) PDF Download
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Author: Graupe Daniel
Publisher: World Scientific
ISBN: 9811201242
Category : Computers
Languages : en
Pages : 440
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Book Description
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.
Author: Graupe Daniel
Publisher: World Scientific
ISBN: 9811201242
Category : Computers
Languages : en
Pages : 440
Get Book Here
Book Description
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.
Author: Daniel Graupe
Publisher: World Scientific
ISBN: 9814475564
Category : Computers
Languages : en
Pages : 320
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Book Description
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.
Author: Daniel Graupe
Publisher: World Scientific
ISBN: 9789810241254
Category : Mathematics
Languages : en
Pages : 256
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Book Description
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.
Author: Daniel A. Roberts
Publisher: Cambridge University Press
ISBN: 1316519333
Category : Computers
Languages : en
Pages : 473
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Book Description
This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Author: Daniel Graupe
Publisher: World Scientific
ISBN: 9812706240
Category : Computers
Languages : en
Pages : 320
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Book Description
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.
Author: Daniel Graupe
Publisher: World Scientific
ISBN: 9814522740
Category : Computers
Languages : en
Pages : 382
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Book Description
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."
Author: Jeremy Rogerson
Publisher:
ISBN: 9781682856697
Category :
Languages : en
Pages : 192
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Book Description
Artificial neural networks refer to the computing systems inspired by biological neural networks. They are based on nodes or artificial neurons, which are a replica of biological neurons found in the brain of animals. This enables them to learn and thereby perform tasks by considering examples. The use of artificial neural networks is vast as they are applied in varied fields like medical diagnosis, speech recognition, computer vision, machine translation, etc. Some common variants include convolutional neural networks, deep stacking networks, deep belief networks, deep predictive coding networks, etc. The theoretical properties of artificial neural networks are capacity, generalization and statistics, computational power, convergence, etc. This book is a valuable compilation of topics, ranging from the basic to the most complex advancements in the field of artificial neural networks. The book attempts to assist those with a goal of delving into this field. The various studies that are constantly contributing towards advancing technologies and evolution of this field are examined in detail.
Author: Daniel Graupe
Publisher: World Scientific
ISBN: 9814522759
Category : Computers
Languages : en
Pages : 382
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Book Description
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.
Author: Xingui He
Publisher: Springer Science & Business Media
ISBN: 3540737626
Category : Computers
Languages : en
Pages : 240
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Book Description
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.
Author: Giuseppe Ciaburro
Publisher: Packt Publishing Ltd
ISBN: 1788399412
Category : Computers
Languages : en
Pages : 264
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Book Description
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.
