Neural Computing Architectures

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Neural Computing Architectures

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Author :
language : en
Publisher:
Release Date : 1989

Neural Computing Architectures written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Artificial intelligence categories.

Neural Computing Architectures

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Author : Igor Aleksander
language : en
Publisher: MIT Press (MA)
Release Date : 2003-02-01

Neural Computing Architectures written by Igor Aleksander and has been published by MIT Press (MA) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-02-01 with Art categories.

An overview and synopsis of European connectionist research from both classical and mathematical perspectives.

Emergent Neural Computational Architectures Based On Neuroscience

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Author : Stefan Wermter
language : en
Publisher: Springer
Release Date : 2003-05-15

Emergent Neural Computational Architectures Based On Neuroscience written by Stefan Wermter and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-05-15 with Computers categories.

It is generally understood that the present approachs to computing do not have the performance, flexibility, and reliability of biological information processing systems. Although there is a comprehensive body of knowledge regarding how information processing occurs in the brain and central nervous system this has had little impact on mainstream computing so far. This book presents a broad spectrum of current research into biologically inspired computational systems and thus contributes towards developing new computational approaches based on neuroscience. The 39 revised full papers by leading researchers were carefully selected and reviewed for inclusion in this anthology. Besides an introductory overview by the volume editors, the book offers topical parts on modular organization and robustness, timing and synchronization, and learning and memory storage.

Neural Computing Architectures

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Author : Igor Aleksander
language : en
Publisher:
Release Date : 1989

Neural Computing Architectures written by Igor Aleksander and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Artificial intelligence categories.

Fundamentals Of Neural Networks Architectures Algorithms And Applications

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Author : Laurene V. Fausett
language : en
Publisher: Pearson Education India
Release Date : 2006

Fundamentals Of Neural Networks Architectures Algorithms And Applications written by Laurene V. Fausett and has been published by Pearson Education India this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Neural networks (Computer science) categories.

Neural Network Analysis Architectures And Applications

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Author : A Browne
language : en
Publisher: CRC Press
Release Date : 1997-01-01

Neural Network Analysis Architectures And Applications written by A Browne and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-01-01 with Mathematics categories.

Neural Network Analysis, Architectures and Applications discusses the main areas of neural networks, with each authoritative chapter covering the latest information from different perspectives. Divided into three parts, the book first lays the groundwork for understanding and simplifying networks. It then describes novel architectures and algorithms, including pulse-stream techniques, cellular neural networks, and multiversion neural computing. The book concludes by examining various neural network applications, such as neuron-fuzzy control systems and image compression. This final part of the book also provides a case study involving oil spill detection. This book is invaluable for students and practitioners who have a basic understanding of neural computing yet want to broaden and deepen their knowledge of the field.

Hands On Deep Learning Architectures With Python

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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-30

Hands On Deep Learning Architectures With Python written by Yuxi (Hayden) Liu 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 2019-04-30 with Computers categories.

Concepts, tools, and techniques to explore deep learning architectures and methodologies Key FeaturesExplore advanced deep learning architectures using various datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns and different challenges for various deep learning architecturesBook Description Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world. What you will learnImplement CNNs, RNNs, and other commonly used architectures with PythonExplore architectures such as VGGNet, AlexNet, and GoogLeNetBuild deep learning architectures for AI applications such as face and image recognition, fraud detection, and many moreUnderstand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architectureUnderstand deep learning architectures for mobile and embedded systemsWho this book is for If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book

Hybrid Architectures For Intelligent Systems

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Author : Abraham Kandel
language : en
Publisher: CRC Press
Release Date : 2020-09-10

Hybrid Architectures For Intelligent Systems written by Abraham Kandel and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-10 with Computers categories.

Hybrid architecture for intelligent systems is a new field of artificial intelligence concerned with the development of the next generation of intelligent systems. This volume is the first book to delineate current research interests in hybrid architectures for intelligent systems. The book is divided into two parts. The first part is devoted to the theory, methodologies, and algorithms of intelligent hybrid systems. The second part examines current applications of intelligent hybrid systems in areas such as data analysis, pattern classification and recognition, intelligent robot control, medical diagnosis, architecture, wastewater treatment, and flexible manufacturing systems. Hybrid Architectures for Intelligent Systems is an important reference for computer scientists and electrical engineers involved with artificial intelligence, neural networks, parallel processing, robotics, and systems architecture.

Neural Computing Architectures For Adaptive Learning Control Systems

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Author : Ali Ismael
language : en
Publisher:
Release Date : 1989

Neural Computing Architectures For Adaptive Learning Control Systems written by Ali Ismael and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Adaptive control systems categories.

Computational Architectures Integrating Neural And Symbolic Processes

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Author : Ron Sun
language : en
Publisher: Springer
Release Date : 2007-08-19

Computational Architectures Integrating Neural And Symbolic Processes written by Ron Sun and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-19 with Computers categories.

Computational Architectures Integrating Neural and Symbolic Processes: A Perspective on the State of the Art focuses on a currently emerging body of research. With the reemergence of neural networks in the 1980s with their emphasis on overcoming some of the limitations of symbolic AI, there is clearly a need to support some form of high-level symbolic processing in connectionist networks. As argued by many researchers, on both the symbolic AI and connectionist sides, many cognitive tasks, e.g. language understanding and common sense reasoning, seem to require high-level symbolic capabilities. How these capabilities are realized in connectionist networks is a difficult question and it constitutes the focus of this book. Computational Architectures Integrating Neural and Symbolic Processes addresses the underlying architectural aspects of the integration of neural and symbolic processes. In order to provide a basis for a deeper understanding of existing divergent approaches and provide insight for further developments in this field, this book presents: (1) an examination of specific architectures (grouped together according to their approaches), their strengths and weaknesses, why they work, and what they predict, and (2) a critique/comparison of these approaches. Computational Architectures Integrating Neural and Symbolic Processes is of interest to researchers, graduate students, and interested laymen, in areas such as cognitive science, artificial intelligence, computer science, cognitive psychology, and neurocomputing, in keeping up-to-date with the newest research trends. It is a comprehensive, in-depth introduction to this new emerging field.