Guide To Neural Computing Applications

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Guide to Neural Computing Applications

Author : Lionel Tarassenko
Publisher : Elsevier
ISBN 13 : 0080512607
Total Pages : 160 pages
Book Rating : 4.0/5 (85 download)

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Book Synopsis Guide to Neural Computing Applications by : Lionel Tarassenko

Download or read book Guide to Neural Computing Applications written by Lionel Tarassenko and published by Elsevier. This book was released on 1998-01-30 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks have shown enormous potential for commercial exploitation over the last few years but it is easy to overestimate their capabilities. A few simple algorithms will learn relationships between cause and effect or organise large volumes of data into orderly and informative patterns but they cannot solve every problem and consequently their application must be chosen carefully and appropriately. This book outlines how best to make use of neural networks. It enables newcomers to the technology to construct robust and meaningful non-linear models and classifiers and benefits the more experienced practitioner who, through over familiarity, might otherwise be inclined to jump to unwarranted conclusions. The book is an invaluable resource not only for those in industry who are interested in neural computing solutions, but also for final year undergraduates or graduate students who are working on neural computing projects. It provides advice which will help make the best use of the growing number of commercial and public domain neural network software products, freeing the specialist from dependence upon external consultants.

Handbook of Neural Computing Applications

Author : Alianna J. Maren
Publisher : Academic Press
ISBN 13 : 148326484X
Total Pages : 472 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Handbook of Neural Computing Applications by : Alianna J. Maren

Download or read book Handbook of Neural Computing Applications written by Alianna J. Maren and published by Academic Press. This book was released on 2014-05-10 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Neural Computing Applications is a collection of articles that deals with neural networks. Some papers review the biology of neural networks, their type and function (structure, dynamics, and learning) and compare a back-propagating perceptron with a Boltzmann machine, or a Hopfield network with a Brain-State-in-a-Box network. Other papers deal with specific neural network types, and also on selecting, configuring, and implementing neural networks. Other papers address specific applications including neurocontrol for the benefit of control engineers and for neural networks researchers. Other applications involve signal processing, spatio-temporal pattern recognition, medical diagnoses, fault diagnoses, robotics, business, data communications, data compression, and adaptive man-machine systems. One paper describes data compression and dimensionality reduction methods that have characteristics, such as high compression ratios to facilitate data storage, strong discrimination of novel data from baseline, rapid operation for software and hardware, as well as the ability to recognized loss of data during compression or reconstruction. The collection can prove helpful for programmers, computer engineers, computer technicians, and computer instructors dealing with many aspects of computers related to programming, hardware interface, networking, engineering or design.

Applying Neural Networks

Author : Kevin Swingler
Publisher : Morgan Kaufmann
ISBN 13 : 9780126791709
Total Pages : 348 pages
Book Rating : 4.7/5 (917 download)

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Book Synopsis Applying Neural Networks by : Kevin Swingler

Download or read book Applying Neural Networks written by Kevin Swingler and published by Morgan Kaufmann. This book was released on 1996 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed to enable the reader to design and run a neural network-based project. It presents everything the reader will need to know to ensure the success of such a project. The book contains a free disk with C and C++ programs, which implement many of the techniques discussed in the book.

Guide to Convolutional Neural Networks

Author : Hamed Habibi Aghdam
Publisher : Springer
ISBN 13 : 3319575503
Total Pages : 282 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Guide to Convolutional Neural Networks by : Hamed Habibi Aghdam

Download or read book Guide to Convolutional Neural Networks written by Hamed Habibi Aghdam and published by Springer. This book was released on 2017-05-17 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website. This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.

Introduction To The Theory Of Neural Computation

Author : John A. Hertz
Publisher : CRC Press
ISBN 13 : 0429968213
Total Pages : 352 pages
Book Rating : 4.4/5 (299 download)

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Book Synopsis Introduction To The Theory Of Neural Computation by : John A. Hertz

Download or read book Introduction To The Theory Of Neural Computation written by John A. Hertz and published by CRC Press. This book was released on 2018-03-08 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.

Neural Networks and Deep Learning

Author : Charu C. Aggarwal
Publisher : Springer
ISBN 13 : 3319944630
Total Pages : 497 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Neural Networks and Deep Learning by : Charu C. Aggarwal

Download or read book Neural Networks and Deep Learning written by Charu C. Aggarwal and published by Springer. This book was released on 2018-08-25 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Convolutional Neural Networks in Visual Computing

Author : Ragav Venkatesan
Publisher : CRC Press
ISBN 13 : 1351650327
Total Pages : 204 pages
Book Rating : 4.3/5 (516 download)

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Book Synopsis Convolutional Neural Networks in Visual Computing by : Ragav Venkatesan

Download or read book Convolutional Neural Networks in Visual Computing written by Ragav Venkatesan and published by CRC Press. This book was released on 2017-10-23 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.

A Practical Guide to Neural Nets

Author : Marilyn McCord Nelson
Publisher : Addison Wesley Publishing Company
ISBN 13 :
Total Pages : 360 pages
Book Rating : 4.0/5 ( download)

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Book Synopsis A Practical Guide to Neural Nets by : Marilyn McCord Nelson

Download or read book A Practical Guide to Neural Nets written by Marilyn McCord Nelson and published by Addison Wesley Publishing Company. This book was released on 1994 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on a course given to internal managers at Texas Instruments, this book is an introduction to neural nets for computer science, artificial intelligence and R & D professionals, as well as MIS or DP managers.

Artificial Neural Networks

Author : Ivan Nunes da Silva
Publisher : Springer
ISBN 13 : 3319431625
Total Pages : 307 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Artificial Neural Networks by : Ivan Nunes da Silva

Download or read book Artificial Neural Networks written by Ivan Nunes da Silva and published by Springer. This book was released on 2016-08-24 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

Neural Networks

Author : Michael Chester
Publisher : Prentice Hall
ISBN 13 :
Total Pages : 196 pages
Book Rating : 4.:/5 (319 download)

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Book Synopsis Neural Networks by : Michael Chester

Download or read book Neural Networks written by Michael Chester and published by Prentice Hall. This book was released on 1993 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Summarizing the status of the neural network field today, this comprehensive volume presents the software-based paradigms and the hardware implementations of neural networks and how they function. Michael Chester describes the mathematical foundations of the various neural network models, as well as those of fuzzy theory." "Sample applications are described in such areas as optical recognition of handwritten characters, speech recognition, machine control, and simulated biological evolution. Diagrams show neurons, their interconnections, and their behavior, as input patterns are presented to the networks. The step-by-step operations of networks are shown, both in their learning modes and their recall modes, as they categorize new input patterns on the basis of what was learned in the earlier phase." "Neural Networks: A Tutorial provides the ideal bridge from theoretical background to practical understanding of neural networks."--BOOK JACKET.Title Summary field provided by Blackwell North America, Inc. All Rights Reserved

Neural Networks

Author : Berndt Müller
Publisher : Springer Science & Business Media
ISBN 13 : 3642577601
Total Pages : 340 pages
Book Rating : 4.6/5 (425 download)

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Book Synopsis Neural Networks by : Berndt Müller

Download or read book Neural Networks written by Berndt Müller and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Building Neural Networks

Author : David M. Skapura
Publisher : Addison-Wesley Professional
ISBN 13 : 9780201539219
Total Pages : 308 pages
Book Rating : 4.5/5 (392 download)

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Book Synopsis Building Neural Networks by : David M. Skapura

Download or read book Building Neural Networks written by David M. Skapura and published by Addison-Wesley Professional. This book was released on 1996 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Organized by application areas, rather than by specific network architectures or learning algorithms, Building Neural Networks shows why certain networks are more suitable than others for solving specific kinds of problems. Skapura also reviews principles of neural information processing and furnishes an operations summary of the most popular neural-network processing models.

Geophysical Applications of Artificial Neural Networks and Fuzzy Logic

Author : W. Sandham
Publisher : Springer Science & Business Media
ISBN 13 : 9401702713
Total Pages : 336 pages
Book Rating : 4.4/5 (17 download)

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Book Synopsis Geophysical Applications of Artificial Neural Networks and Fuzzy Logic by : W. Sandham

Download or read book Geophysical Applications of Artificial Neural Networks and Fuzzy Logic written by W. Sandham and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past fifteen years has witnessed an explosive growth in the fundamental research and applications of artificial neural networks (ANNs) and fuzzy logic (FL). The main impetus behind this growth has been the ability of such methods to offer solutions not amenable to conventional techniques, particularly in application domains involving pattern recognition, prediction and control. Although the origins of ANNs and FL may be traced back to the 1940s and 1960s, respectively, the most rapid progress has only been achieved in the last fifteen years. This has been due to significant theoretical advances in our understanding of ANNs and FL, complemented by major technological developments in high-speed computing. In geophysics, ANNs and FL have enjoyed significant success and are now employed routinely in the following areas (amongst others): 1. Exploration Seismology. (a) Seismic data processing (trace editing; first break picking; deconvolution and multiple suppression; wavelet estimation; velocity analysis; noise identification/reduction; statics analysis; dataset matching/prediction, attenuation), (b) AVO analysis, (c) Chimneys, (d) Compression I dimensionality reduction, (e) Shear-wave analysis, (f) Interpretation (event tracking; lithology prediction and well-log analysis; prospect appraisal; hydrocarbon prediction; inversion; reservoir characterisation; quality assessment; tomography). 2. Earthquake Seismology and Subterranean Nuclear Explosions. 3. Mineral Exploration. 4. Electromagnetic I Potential Field Exploration. (a) Electromagnetic methods, (b) Potential field methods, (c) Ground penetrating radar, (d) Remote sensing, (e) inversion.

Business Applications of Neural Networks

Author : Bill Edisbury
Publisher : World Scientific
ISBN 13 : 9812813314
Total Pages : 222 pages
Book Rating : 4.8/5 (128 download)

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Book Synopsis Business Applications of Neural Networks by : Bill Edisbury

Download or read book Business Applications of Neural Networks written by Bill Edisbury and published by World Scientific. This book was released on 2000 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are increasingly being used in real-world business applications and, in some cases, such as fraud detection, they have already become the method of choice. Their use for risk assessment is also growing and they have been employed to visualise complex databases for marketing segmentation. This boom in applications covers a wide range of business interests - from finance management, through forecasting, to production. The combination of statistical, neural and fuzzy methods now enables direct quantitative studies to be carried out without the need for rocket-science expertise. This is a review of the state-of-the-art in applications of neural-network methods in three important areas of business analysis. It includes a tutorial chapter to introduce new users to the potential and pitfalls of this new technology.

Artificial Neural Networks

Author : David J. Livingstone
Publisher : Humana Press
ISBN 13 : 9781617377389
Total Pages : 0 pages
Book Rating : 4.3/5 (773 download)

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Book Synopsis Artificial Neural Networks by : David J. Livingstone

Download or read book Artificial Neural Networks written by David J. Livingstone and published by Humana Press. This book was released on 2011-10-09 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, international experts report the history of the application of ANN to chemical and biological problems, provide a guide to network architectures, training and the extraction of rules from trained networks, and cover many cutting-edge examples of the application of ANN to chemistry and biology. Methods involving the mapping and interpretation of Infra Red spectra and modelling environmental toxicology are included. This book is an excellent guide to this exciting field.

A Guide to Convolutional Neural Networks for Computer Vision

Author : Salman Khan
Publisher : Springer Nature
ISBN 13 : 3031018214
Total Pages : 187 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis A Guide to Convolutional Neural Networks for Computer Vision by : Salman Khan

Download or read book A Guide to Convolutional Neural Networks for Computer Vision written by Salman Khan and published by Springer Nature. This book was released on 2022-06-01 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.

Programming with TensorFlow

Author : Kolla Bhanu Prakash
Publisher : Springer Nature
ISBN 13 : 3030570770
Total Pages : 190 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Programming with TensorFlow by : Kolla Bhanu Prakash

Download or read book Programming with TensorFlow written by Kolla Bhanu Prakash and published by Springer Nature. This book was released on 2021-01-22 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics. The book provides a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. The authors begin by working through some basic examples in TensorFlow before diving deeper into topics such as CNN, RNN, LSTM, and GNN. The book is written for those who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. The authors demonstrate TensorFlow projects on Single Board Computers (SBCs).