Programming Neural Networks With Encog 3 In Java
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Programming Neural Networks With Encog 3 In Java
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Author : Jeff Heaton
language : en
Publisher:
Release Date : 2011
Programming Neural Networks With Encog 3 In Java written by Jeff Heaton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
Beginning where our introductory neural network programing book left off, this book introduces you to Encog. Encog allows you to focus less on the actual implementation of neural networks and focus on how to use them. Encog is an advanced neural network programming framework that allows you to create a variety of neural network architectures using the Java programming language. Neural network architectures such as feedforward/perceptrons, Hopfield, Elman, Jordan, Radial Basis Function, and Self Organizing maps are all demonstrated. This book also shows how to use Encog to train neural networks using a variety of means. Several propagation techniques, such as back propagation, resilient propagation (RPROP) and the Manhattan update rule are discussed. Additionally, training with a genetic algorithm and simulated annealing is discussed as well. You will also see how to enhance training using techniques such as pruning and hybrid training.
Programming Neural Networks With Encog 3 In C
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Author : Jeff Heaton
language : en
Publisher:
Release Date : 2011
Programming Neural Networks With Encog 3 In C written by Jeff Heaton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
This book focuses on using the neural network capabilities of Encog with the C# programming language. The reader is shown how to use classification, regression, and clustering to gain new insights into data.
Programming Neural Networks With Encog 2 In Java
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Author : Jeff Heaton
language : en
Publisher:
Release Date : 2009-12
Programming Neural Networks With Encog 2 In Java written by Jeff Heaton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-12 with C# (Computer program language) categories.
Encog is an advanced neural network and bot programming framework. This book focuses on using Encog to create a variety of neural network architectures using the Java programming language. Neural network architectures such as feedforward/perceptrons, Hopfield, Elman, Jordan, Radial Basis Function, and Self Organizing maps are all demonstrated. This book also shows how to use Encog to train neural networks using a variety of means. Several propagation techniques, such as back propagation, resilient propagation (RPROP) and the Manhattan update rule are discussed. Additionally, training with a genetic algorithm and simulated annealing is discussed as well. You will also see how to enhance training using techniques such as pruning, hybrid training, Real world examples tie the book together. Pattern recognition applications such as OCR, image and text recognition will be introduced. You will see how to apply time series and forecasting and how to financial markets. All of the Encog neural network components will be demonstrated to show how to use them in your own neural network applications.
Artificial Neural Networks With Java
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Author : Igor Livshin
language : en
Publisher:
Release Date : 2022
Artificial Neural Networks With Java written by Igor Livshin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Develop neural network applications using the Java environment. After learning the rules involved in neural network processing, this second edition shows you how to manually process your first neural network example. The book covers the internals of front and back propagation and helps you understand the main principles of neural network processing. You also will learn how to prepare the data to be used in neural network development and you will be able to suggest various techniques of data preparation for many unconventional tasks. This book discusses the practical aspects of using Java for neural network processing. You will know how to use the Encog Java framework for processing large-scale neural network applications. Also covered is the use of neural networks for approximation of non-continuous functions. In addition to using neural networks for regression, this second edition shows you how to use neural networks for computer vision. It focuses on image recognition such as the classification of handwritten digits, input data preparation and conversion, and building the conversion program. And you will learn about topics related to the classification of handwritten digits such as network architecture, program code, programming logic, and execution. The step-by-step approach taken in the book includes plenty of examples, diagrams, and screenshots to help you grasp the concepts quickly and easily. What You Will Learn Use Java for the development of neural network applications Prepare data for many different tasks Carry out some unusual neural network processing Use a neural network to process non-continuous functions Develop a program that recognizes handwritten digits.
Introduction To Neural Networks With Java
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Author : Jeff Heaton
language : en
Publisher: Heaton Research Incorporated
Release Date : 2005
Introduction To Neural Networks With Java written by Jeff Heaton and has been published by Heaton Research Incorporated this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.
In addition to showing the programmer how to construct Neural Networks, the book discusses the Java Object Oriented Neural Engine (JOONE), a free open source Java neural engine. (Computers)
Introduction To Neural Networks With Java
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Author : Jeff Heaton
language : en
Publisher: Heaton Research, Inc.
Release Date : 2008
Introduction To Neural Networks With Java written by Jeff Heaton and has been published by Heaton Research, Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computers categories.
Introduction to Neural Networks in Java, Second Edition, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures such as the feedforward, Hopfield, and Self Organizing Map networks are discussed. Training techniques such as Backpropagation, Genetic Algorithms and Simulated Annealing are also introduced. Practical examples are given for each neural network. Examples include the Traveling Salesman problem, handwriting recognition, financial prediction, game strategy, learning mathematical functions and special application to Internet bots. All Java source code can be downloaded online.
Eeg Brain Signal Classification For Epileptic Seizure Disorder Detection
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Author : Sandeep Kumar Satapathy
language : en
Publisher: Academic Press
Release Date : 2019-02-10
Eeg Brain Signal Classification For Epileptic Seizure Disorder Detection written by Sandeep Kumar Satapathy and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-10 with Medical categories.
EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques. Chapters present an overview of machine learning techniques and the tools available, discuss previous studies, present empirical studies on the performance of the NN and SVM classifiers, discuss RBF neural networks trained with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final chapter present future developments in the field. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need the most recent and promising automated techniques for EEG classification. Explores machine learning techniques that have been modified and validated for the purpose of EEG signal classification using Discrete Wavelet Transform for the identification of epileptic seizures Encompasses machine learning techniques, providing an easily understood resource for both non-specialized readers and biomedical researchers Provides a number of experimental analyses, with their results discussed and appropriately validated
Mastering Java For Data Science
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Author : Alexey Grigorev
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-04-27
Mastering Java For Data Science written by Alexey Grigorev 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 2017-04-27 with Computers categories.
Use Java to create a diverse range of Data Science applications and bring Data Science into production About This Book An overview of modern Data Science and Machine Learning libraries available in Java Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks. Easy-to-follow illustrations and the running example of building a search engine. Who This Book Is For This book is intended for software engineers who are comfortable with developing Java applications and are familiar with the basic concepts of data science. Additionally, it will also be useful for data scientists who do not yet know Java but want or need to learn it. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing the existing stack, this book is for you! What You Will Learn Get a solid understanding of the data processing toolbox available in Java Explore the data science ecosystem available in Java Find out how to approach different machine learning problems with Java Process unstructured information such as natural language text or images Create your own search engine Get state-of-the-art performance with XGBoost Learn how to build deep neural networks with DeepLearning4j Build applications that scale and process large amounts of data Deploy data science models to production and evaluate their performance In Detail Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings. Style and approach This is a practical guide where all the important concepts such as classification, regression, and dimensionality reduction are explained with the help of examples.
Computer Information Systems And Industrial Management
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Author : Khalid Saeed
language : en
Publisher: Springer
Release Date : 2013-09-20
Computer Information Systems And Industrial Management written by Khalid Saeed and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-20 with Computers categories.
This book constitutes the proceedings of the 12th IFIP TC 8 International Conference, CISIM 2013, held in Cracow, Poland, in September 2013. The 44 papers presented in this volume were carefully reviewed and selected from over 60 submissions. They are organized in topical sections on biometric and biomedical applications; pattern recognition and image processing; various aspects of computer security, networking, algorithms, and industrial applications. The book also contains full papers of a keynote speech and the invited talk.
Hands On Neural Network Programming With C
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Author : Matt R. Cole
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-09-29
Hands On Neural Network Programming With C written by Matt R. Cole 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 2018-09-29 with Computers categories.
Create and unleash the power of neural networks by implementing C# and .Net code Key FeaturesGet a strong foundation of neural networks with access to various machine learning and deep learning librariesReal-world case studies illustrating various neural network techniques and architectures used by practitionersCutting-edge coverage of Deep Networks, optimization algorithms, convolutional networks, autoencoders and many moreBook Description Neural networks have made a surprise comeback in the last few years and have brought tremendous innovation in the world of artificial intelligence. The goal of this book is to provide C# programmers with practical guidance in solving complex computational challenges using neural networks and C# libraries such as CNTK, and TensorFlowSharp. This book will take you on a step-by-step practical journey, covering everything from the mathematical and theoretical aspects of neural networks, to building your own deep neural networks into your applications with the C# and .NET frameworks. This book begins by giving you a quick refresher of neural networks. You will learn how to build a neural network from scratch using packages such as Encog, Aforge, and Accord. You will learn about various concepts and techniques, such as deep networks, perceptrons, optimization algorithms, convolutional networks, and autoencoders. You will learn ways to add intelligent features to your .NET apps, such as facial and motion detection, object detection and labeling, language understanding, knowledge, and intelligent search. Throughout this book, you will be working on interesting demonstrations that will make it easier to implement complex neural networks in your enterprise applications. What you will learnUnderstand perceptrons and how to implement them in C#Learn how to train and visualize a neural network using cognitive servicesPerform image recognition for detecting and labeling objects using C# and TensorFlowSharpDetect specific image characteristics such as a face using Accord.NetDemonstrate particle swarm optimization using a simple XOR problem and EncogTrain convolutional neural networks using ConvNetSharpFind optimal parameters for your neural network functions using numeric and heuristic optimization techniques.Who this book is for This book is for Machine Learning Engineers, Data Scientists, Deep Learning Aspirants and Data Analysts who are now looking to move into advanced machine learning and deep learning with C#. Prior knowledge of machine learning and working experience with C# programming is required to take most out of this book