Neural Network
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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.
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.
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.
Advances In Neural Network Research And Applications
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Author : Zhigang Zeng
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-05-10
Advances In Neural Network Research And Applications written by Zhigang Zeng and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-05-10 with Technology & Engineering categories.
This book is a part of the Proceedings of the Seventh International Symposium on Neural Networks (ISNN 2010), held on June 6-9, 2010 in Shanghai, China. Over the past few years, ISNN has matured into a well-established premier international symposium on neural networks and related fields, with a successful sequence of ISNN series in Dalian (2004), Chongqing (2005), Chengdu (2006), Nanjing (2007), Beijing (2008), and Wuhan (2009). Following the tradition of ISNN series, ISNN 2010 provided a high-level international forum for scientists, engineers, and educators to present the state-of-the-art research in neural networks and related fields, and also discuss the major opportunities and challenges of future neural network research. Over the past decades, the neural network community has witnessed significant breakthroughs and developments from all aspects of neural network research, including theoretical foundations, architectures, and network organizations, modeling and simulation, empirical studies, as well as a wide range of applications across different domains. The recent developments of science and technology, including neuroscience, computer science, cognitive science, nano-technologies and engineering design, among others, has provided significant new understandings and technological solutions to move the neural network research toward the development of complex, large scale, and networked brain-like intelligent systems. This long-term goals can only be achieved with the continuous efforts from the community to seriously investigate various issues on neural networks and related topics.
Introduction To Neural Networks
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Author : Architecture Technology Architecture Technology Corpor
language : en
Publisher: Elsevier
Release Date : 2015-11-24
Introduction To Neural Networks written by Architecture Technology Architecture Technology Corpor and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-24 with Computers categories.
Please note this is a Short Discount publication. Neural network technology has been a curiosity since the early days of computing. Research in the area went into a near dormant state for a number of years, but recently there has been a new increased interest in the subject. This has been due to a number of factors: interest in the military, apparent ease of implementation, and the ability of the technology to develop computers which are able to learn from experience. This report summarizes the topic, providing the reader with an overview of the field and its potential direction. Included is an introduction to the technology and its future directions, as well as a set of examples of possible applications and potential implementation technologies.
Deep Learning With Pytorch
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Author : Vishnu Subramanian
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-02-23
Deep Learning With Pytorch written by Vishnu Subramanian 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 2018-02-23 with Computers categories.
Build neural network models in text, vision and advanced analytics using PyTorch Key Features Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Book Description Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease. What you will learn Use PyTorch for GPU-accelerated tensor computations Build custom datasets and data loaders for images and test the models using torchvision and torchtext Build an image classifier by implementing CNN architectures using PyTorch Build systems that do text classification and language modeling using RNN, LSTM, and GRU Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning Learn how to mix multiple models for a powerful ensemble model Generate new images using GAN’s and generate artistic images using style transfer Who this book is for This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.
Neural Network Computing
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Author : Ramachandran Bharath
language : en
Publisher: McGraw-Hill Companies
Release Date : 1994
Neural Network Computing written by Ramachandran Bharath and has been published by McGraw-Hill Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Computers categories.
An introduction to neural networking for systems designers, software developers, programmers, and advanced hobbyists. The authors explain how "brain-style" computing will revolutionize information processing in the 21st century. The disk includes programs for simulating artificial neural networks.
Artificial Neural Networks Icann 2008
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Author : Vera Kůrková
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-08-25
Artificial Neural Networks Icann 2008 written by Vera Kůrková and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-25 with Computers categories.
This two volume set LNCS 5163 and LNCS 5164 constitutes the refereed proceedings of the 18th International Conference on Artificial Neural Networks, ICANN 2008, held in Prague Czech Republic, in September 2008. The 200 revised full papers presented were carefully reviewed and selected from more than 300 submissions. The first volume contains papers on mathematical theory of neurocomputing, learning algorithms, kernel methods, statistical learning and ensemble techniques, support vector machines, reinforcement learning, evolutionary computing, hybrid systems, self-organization, control and robotics, signal and time series processing and image processing.
Neural Networks In Optimization
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Author : Xiang-Sun Zhang
language : en
Publisher: Springer Science & Business Media
Release Date : 2000-10-31
Neural Networks In Optimization written by Xiang-Sun Zhang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-10-31 with Business & Economics categories.
The book consists of three parts. The first part introduces concepts and algorithms in optimization theory, which have been used in neural network research. The second part covers main neural network models and their theoretical analysis. The third part of the book introduces various neural network models for solving nonlinear programming problems and combinatorial optimization problems. Audience: Graduate students and researchers who are interested in the intersection of optimization theory and artificial neural networks. The book is appropriate for graduate courses.
Neural Network Algorithms And Their Engineering Applications
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Author : Chao Huang
language : en
Publisher: Morgan Kaufmann
Release Date : 2025-01-23
Neural Network Algorithms And Their Engineering Applications written by Chao Huang and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-23 with Computers categories.
Neural Network Algorithms and Their Engineering Applications presents the relevant techniques used to improve the global search ability of neural network algorithms in solving complex engineering problems with multimodal properties. The book provides readers with a complete study of how to use artificial neural networks to design a population-based metaheuristic algorithm, which in turn promotes the application of artificial neural networks in the field of engineering optimization.The authors provide a deep discussion for the potential application of machine learning methods in improving the optimization performance of the neural network algorithm, helping readers understand how to use machine learning methods to design improved versions of the algorithm. Users will find a wealth of source code that covers all applied algorithms. Code applications enhance readers' understanding of methods covered and facilitate readers' ability to apply the algorithms to their own research and development projects. - Provides a comprehensive understanding of the development of metaheuristics, helping readers grasp the principle of employing artificial neural networks to design a population-based metaheuristic algorithm - Shows readers how to overcome the challenges faced in applying neural network algorithms to complex engineering optimization problems with multimodal properties - Demonstrates how to design new variants of neural network algorithms and how to apply machine learning methods to neural network algorithms - Covers source code to help readers solve engineering optimization problems - Shows readers how to develop the offered source code to create innovative solutions to their problems
Neural Networks
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Author : Berndt Müller
language : en
Publisher: Springer Science & Business Media
Release Date : 1995-10-02
Neural Networks written by Berndt Müller and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-10-02 with Computers categories.
Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.