Neural Network Parallel Computing
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Neural Network Parallel Computing
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Author : Yoshiyasu Takefuji
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Neural Network Parallel Computing written by Yoshiyasu Takefuji 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 2012-12-06 with Technology & Engineering categories.
Neural Network Parallel Computing is the first book available to the professional market on neural network computing for optimization problems. This introductory book is not only for the novice reader, but for experts in a variety of areas including parallel computing, neural network computing, computer science, communications, graph theory, computer aided design for VLSI circuits, molecular biology, management science, and operations research. The goal of the book is to facilitate an understanding as to the uses of neural network models in real-world applications. Neural Network Parallel Computing presents a major breakthrough in science and a variety of engineering fields. The computational power of neural network computing is demonstrated by solving numerous problems such as N-queen, crossbar switch scheduling, four-coloring and k-colorability, graph planarization and channel routing, RNA secondary structure prediction, knight's tour, spare allocation, sorting and searching, and tiling. Neural Network Parallel Computing is an excellent reference for researchers in all areas covered by the book. Furthermore, the text may be used in a senior or graduate level course on the topic.
Parallel Processing In Neural Systems And Computers
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Author : Rolf Eckmiller
language : en
Publisher: North Holland
Release Date : 1990
Parallel Processing In Neural Systems And Computers written by Rolf Eckmiller and has been published by North Holland this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Computers categories.
The 119 contributions in this book cover a range of topics, including parallel computing, parallel processing in biological neural systems, simulators for artificial neural networks, neural networks for visual and auditory pattern recognition as well as for motor control, AI, and examples of optical and molecular computing. The book may be regarded as a state-of-the-art report and at the same time as an Interdisciplinary Reference Source' for parallel processing. It should catalyze international and interdisciplinary cooperation among computer scientists, neuroscientists, physicists and engineers in the attempt to: 1) decipher parallel information processes in biology, physics and chemistry 2) design conceptually similar technical parallel information processors."
Parallel Architectures For Artificial Neural Networks
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Author : N. Sundararajan
language : en
Publisher: Wiley-IEEE Computer Society Press
Release Date : 1998-12-14
Parallel Architectures For Artificial Neural Networks written by N. Sundararajan and has been published by Wiley-IEEE Computer Society Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-12-14 with Computers categories.
An excellent reference for neural networks research and application, this book covers the parallel implementation aspects of all major artificial neural network models in a single text. Parallel Architectures for Artificial Neural Networks details implementations on various processor architectures built on different hardware platforms, ranging from large, general purpose parallel computers to custom built MIMD machine. Working experts describe their implementation research including results that are then divided into three sections: The theoretical analysis of parallel implementation schemes on MIMD message passing machines The details of parallel implementation of BP neural networks on general purpose, large, parallel computers Four specific purpose parallel neural computer configuration Aimed at graduate students and researchers working in artificial neural networks and parallel computing this work can be used by graduate level educators to illustrate parallel computing methods for ANN simulation. Practitioners will also find the text an ideal reference tool for lucid mathematical analyses.
Neural Network Parallel Computing For Optimization Problems
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Author : Kuo-chun Lee
language : en
Publisher:
Release Date : 1991
Neural Network Parallel Computing For Optimization Problems written by Kuo-chun Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with categories.
Advenced Neural Networks With Matlab
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Author : L. Abell
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-05-29
Advenced Neural Networks With Matlab written by L. Abell and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-29 with categories.
MATLAB Neural Network Toolbox provides algorithms, pretrained models, and apps to create, train, visualize, and simulate both shallow and deep neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. Deep learning networks include convolutional neural networks (ConvNets, CNNs) and autoencoders for image classification, regression, and feature learning. For small training sets, you can quickly apply deep learning by performing transfer learning with pretrained deep networks. To speed up training on large datasets, you can use Parallel Computing Toolbox to distribute computations and data across multicore processors and GPUs on the desktop, and you can scale up to clusters and clouds (including Amazon EC2(R) P2 GPU instances) with MATLAB(R) Distributed Computing Server. The Key Features developed in this book are de next: - Deep learning with convolutional neural networks (for classification and regression) and autoencoders (for feature learning) - Transfer learning with pretrained convolutional neural network models - Training and inference with CPUs or multi-GPUs on desktops, clusters, and clouds - Unsupervised learning algorithms, including self-organizing maps and competitive layers - Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) - Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance
Advanced Topics In Neural Networks With Matlab Parallel Computing Optimize And Training
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Author : C. Pérez
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-07-31
Advanced Topics In Neural Networks With Matlab Parallel Computing Optimize And Training written by C. Pérez and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-31 with Computers categories.
Neural networks are inherently parallel algorithms. Multicore CPUs, graphical processing units (GPUs), and clusters of computers with multiple CPUs and GPUs can take advantage of this parallelism. Parallel Computing Toolbox, when used in conjunction with Neural Network Toolbox, enables neural network training and simulation to take advantage of each mode of parallelism. Parallel Computing Toolbox allows neural network training and simulation to run across multiple CPU cores on a single PC, or across multiple CPUs on multiple computers on a network using MATLAB Distributed Computing Server. Using multiple cores can speed calculations. Using multiple computers can allow you to solve problems using data sets too big to fit in the RAM of a single computer. The only limit to problem size is the total quantity of RAM available across all computers. Distributed and GPU computing can be combined to run calculations across multiple CPUs and/or GPUs on a single computer, or on a cluster with MATLAB Distributed Computing Server. It is desirable to determine the optimal regularization parameters in an automated fashion. One approach to this process is the Bayesian framework. In this framework, the weights and biases of the network are assumed to be random variables with specified distributions. The regularization parameters are related to the unknown variances associated with these distributions. You can then estimate these parameters using statistical techniques. It is very difficult to know which training algorithm will be the fastest for a given problem. It depends on many factors, including the complexity of the problem, the number of data points in the training set, the number of weights and biases in the network, the error goal, and whether the network is being used for pattern recognition (discriminant analysis) or function approximation (regression). This book compares the various training algorithms. One of the problems that occur during neural network training is called overfitting. The error on the training set is driven to a very small value, but when new data is presented to the network the error is large. The network has memorized the training examples, but it has not learned to generalize to new situations. This book develops the following topics: - "Neural Networks with Parallel and GPU Computing" - "Deep Learning" - "Optimize Neural Network Training Speed and Memory" - "Improve Neural Network Generalization and Avoid Overfitting" - "Create and Train Custom Neural Network Architectures" - "Deploy Training of Neural Networks" - "Perceptron Neural Networks" - "Linear Neural Networks" - "Hopfield Neural Network" - "Neural Network Object Reference" - "Neural Network Simulink Block Library" - "Deploy Neural Network Simulink Diagrams"
High Performance Computing Systems And Applications
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Author : Andrew Pollard
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-18
High Performance Computing Systems And Applications written by Andrew Pollard 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 2006-04-18 with Computers categories.
High Performance Computing Systems and Applications contains the fully refereed papers from the 13th Annual Symposium on High Performance Computing, held in Kingston, Canada, in June 1999. This book presents the latest research in HPC architectures, distributed and shared memory performance, algorithms and solvers, with special sessions on atmospheric science, computational chemistry and physics. High Performance Computing Systems and Applications is suitable as a secondary text for graduate level courses, and as a reference for researchers and practitioners in industry.
Artificial Neural Nets And Genetic Algorithms
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Author : Vera Kurkova
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Artificial Neural Nets And Genetic Algorithms written by Vera Kurkova 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 2013-11-11 with Computers categories.
The first ICANNGA conference, devoted to biologically inspired computational paradigms, Neural Net works and Genetic Algorithms, was held in Innsbruck, Austria, in 1993. The meeting attracted researchers from all over Europe and further afield, who decided that this particular blend of topics should form a theme for a series of biennial conferences. The second meeting, held in Ales, France, in 1995, carried on the tradition set in Innsbruck of a relaxed and stimulating environment for the. exchange of ideas. The series has continued in Norwich, UK, in 1997, and Portoroz, Slovenia, in 1999. The Institute of Computer Science, Czech Academy of Sciences, is pleased to host the fifth conference in Prague. We have chosen the Liechtenstein palace under the Prague Castle as the conference site to enhance the traditionally good atmosphere of the meeting. There is an inspirational genius loci of the historical center of the city, where four hundred years ago a fruitful combination of theoretical and empirical method, through the collaboration of Johannes Kepler and Tycho de Brahe, led to the discovery of the laws of planetary orbits.
Soft Computing And Intelligent Systems
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Author : Madan M. Gupta
language : en
Publisher: Elsevier
Release Date : 1999-10-28
Soft Computing And Intelligent Systems written by Madan M. Gupta and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-10-28 with Computers categories.
The field of soft computing is emerging from the cutting edge research over the last ten years devoted to fuzzy engineering and genetic algorithms. The subject is being called soft computing and computational intelligence. With acceptance of the research fundamentals in these important areas, the field is expanding into direct applications through engineering and systems science.This book cover the fundamentals of this emerging filed, as well as direct applications and case studies. There is a need for practicing engineers, computer scientists, and system scientists to directly apply "fuzzy" engineering into a wide array of devices and systems.
Network And Parallel Computing
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Author : Xiaoxin Tang
language : en
Publisher: Springer Nature
Release Date : 2019-09-28
Network And Parallel Computing written by Xiaoxin Tang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-28 with Computers categories.
This book constitutes the proceedings of the 16th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2019, held in Hohhot, China, in August 2019. The 22 full and 11 short papers presented in this volume were carefully reviewed and selected from 107 submissions. They were organized in topical sections named: graph computing; NOC and networks; neural networks; big data and cloud; HPC; emerging topics; memory and file system.