Neural Network Parallel Computing

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Neural Network Parallel Computing

Yoshiyasu Takefuji
Neural Network Parallel Computing Book PDF

Author : Yoshiyasu Takefuji
Publisher : Springer Science & Business Media
Page : 237 pages
File Size : 54,7 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781461536420

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Neural Network Parallel Computing by Yoshiyasu Takefuji Pdf

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

Rolf Eckmiller,Georg Hartmann,Gert Hauske
Parallel Processing in Neural Systems and Computers Book PDF

Author : Rolf Eckmiller,Georg Hartmann,Gert Hauske
Publisher : North Holland
Page : 652 pages
File Size : 44,9 Mb
Release : 1990
Category : Artificial intelligence
ISBN : UOM:39015017921100

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Parallel Processing in Neural Systems and Computers by Rolf Eckmiller,Georg Hartmann,Gert Hauske Pdf

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

N. Sundararajan,P. Saratchandran
Parallel Architectures for Artificial Neural Networks Book PDF

Author : N. Sundararajan,P. Saratchandran
Publisher : Wiley-IEEE Computer Society Press
Page : 424 pages
File Size : 46,9 Mb
Release : 1998-12-14
Category : Computers
ISBN : UOM:39015046908375

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Parallel Architectures for Artificial Neural Networks by N. Sundararajan,P. Saratchandran Pdf

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.

ADVANCED TOPICS IN NEURAL NETWORKS WITH MATLAB. PARALLEL COMPUTING, OPTIMIZE AND TRAINING

PEREZ C.
ADVANCED TOPICS IN NEURAL NETWORKS WITH MATLAB PARALLEL COMPUTING OPTIMIZE AND TRAINING Book PDF

Author : PEREZ C.
Publisher : CESAR PEREZ
Page : 78 pages
File Size : 48,9 Mb
Release : 2023-12-13
Category : Computers
ISBN : 9781974082049

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ADVANCED TOPICS IN NEURAL NETWORKS WITH MATLAB. PARALLEL COMPUTING, OPTIMIZE AND TRAINING by PEREZ C. Pdf

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

Deep Learning and Parallel Computing Environment for Bioengineering Systems

Arun Kumar Sangaiah
Deep Learning and Parallel Computing Environment for Bioengineering Systems Book PDF

Author : Arun Kumar Sangaiah
Publisher : Academic Press
Page : 280 pages
File Size : 42,9 Mb
Release : 2019-07-26
Category : Computers
ISBN : 9780128172933

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Deep Learning and Parallel Computing Environment for Bioengineering Systems by Arun Kumar Sangaiah Pdf

Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas. Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data

Advenced Neural Networks With Matlab

L. Abell
Advenced Neural Networks With Matlab Book PDF

Author : L. Abell
Publisher : Createspace Independent Publishing Platform
Page : 438 pages
File Size : 40,6 Mb
Release : 2017-05-29
Category : Electronic
ISBN : 1547013044

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Advenced Neural Networks With Matlab by L. Abell Pdf

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

Network and Parallel Computing

Xiaoxin Tang,Quan Chen,Pradip Bose,Weiming Zheng,Jean-Luc Gaudiot
Network and Parallel Computing Book PDF

Author : Xiaoxin Tang,Quan Chen,Pradip Bose,Weiming Zheng,Jean-Luc Gaudiot
Publisher : Springer Nature
Page : 385 pages
File Size : 44,8 Mb
Release : 2019-09-28
Category : Computers
ISBN : 9783030307097

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Network and Parallel Computing by Xiaoxin Tang,Quan Chen,Pradip Bose,Weiming Zheng,Jean-Luc Gaudiot Pdf

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.

DEEP LEARNING with MATLAB. NEURAL NETWORKS by EXAMPLES

Cesar Perez Lopez
DEEP LEARNING with MATLAB NEURAL NETWORKS by EXAMPLES Book PDF

Author : Cesar Perez Lopez
Publisher : CESAR PEREZ
Page : 154 pages
File Size : 54,5 Mb
Release : 2020-09-13
Category : Computers
ISBN : 9781716584848

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DEEP LEARNING with MATLAB. NEURAL NETWORKS by EXAMPLES by Cesar Perez Lopez Pdf

MATLAB has the tool Deep Learning Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets (Big data), you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox.

Parallel Computing Technologies

Viktor Ėmmanuilovich Malyshkin
Parallel Computing Technologies Book PDF

Author : Viktor Ėmmanuilovich Malyshkin
Publisher : Springer Science & Business Media
Page : 480 pages
File Size : 41,9 Mb
Release : 2005-08-18
Category : Computers
ISBN : 9783540281269

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Parallel Computing Technologies by Viktor Ėmmanuilovich Malyshkin Pdf

This book constitutes the refereed proceedings of the 8th International Conference on Parallel Computing Technologies, PaCT 2005, held in Krasnoyarsk, Russia in September 2005. The 38 revised full papers presented together with 1 invited paper were carefully reviewed and selected from 78 submissions. The papers are organized in topical sections on theory, fine-grain parallelism, software, tools, and applications. A broad variety of parallel processing issues and distributed computing in general are addressed as well.

Parallel Computing

Jonathan P. Gray,Fazel Naghdy
Parallel Computing Book PDF

Author : Jonathan P. Gray,Fazel Naghdy
Publisher : IOS Press
Page : 312 pages
File Size : 43,8 Mb
Release : 1995
Category : Computers
ISBN : 9051991967

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Parallel Computing by Jonathan P. Gray,Fazel Naghdy Pdf

The broadening of interest in parallel computing and transputers is reflected this book. Topics discussed include: concurrent programming; graphics and image processing; parallel applications; robotics; and control and software tools. The book also features a collection of abstracts of poster presentations.

Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks

Ioannis Pitas
Parallel Algorithms for Digital Image Processing Computer Vision and Neural Networks Book PDF

Author : Ioannis Pitas
Publisher : Unknown
Page : 424 pages
File Size : 49,6 Mb
Release : 1993-04-09
Category : Computers
ISBN : STANFORD:36105002273584

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Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks by Ioannis Pitas Pdf

World-renowned contributors present papers concerning algorithms used on the latest generation of parallel machines (MIMD). Details key applications running the gamut from medical imaging, visualization and remote sensing to HDTV, demonstrating the large computational complexity necessary to perform these tasks.

Parallel Architectures And Neural Networks: Fourth Italian Workshop

E R Caianiello
Parallel Architectures And Neural Networks Fourth Italian Workshop Book PDF

Author : E R Caianiello
Publisher : World Scientific
Page : 396 pages
File Size : 51,7 Mb
Release : 1991-11-12
Category : Electronic
ISBN : 9789814555784

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Parallel Architectures And Neural Networks: Fourth Italian Workshop by E R Caianiello Pdf

Organized and supported by IIASS and other Italian institutions, this Workshop in Vietri sul Mare has now become a permanent annual event. The aim is to foster closer links among all groups and individuals working in Europe in this inter- and multi-disciplinary area. Topics cover models, architectures and applications.

GPU Parallel Computing for Machine Learning in Python

Yoshiyasu Takefuji
GPU Parallel Computing for Machine Learning in Python Book PDF

Author : Yoshiyasu Takefuji
Publisher : Unknown
Page : 51 pages
File Size : 47,5 Mb
Release : 2017-06-17
Category : Electronic
ISBN : 1521524904

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GPU Parallel Computing for Machine Learning in Python by Yoshiyasu Takefuji Pdf

This book illustrates how to build a GPU parallel computer. If you don't want to waste your time for building, you can buy a built-in-GPU desktop/laptop machine. All you need to do is to install GPU-enabled software for parallel computing. Imagine that we are in the midst of a parallel computing era. The GPU parallel computer is suitable for machine learning, deep (neural network) learning. For example, GeForce GTX1080 Ti is a GPU board with 3584 CUDA cores. Using the GeForce GTX1080 Ti, the performance is roughly 20 times faster than that of an INTEL i7 quad-core CPU. We have benchmarked the MNIST hand-written digits recognition problem (60,000 persons: hand-written digits from 0 to 9). The result of MNIST benchmark for machine learning shows that GPU of a single GeForce GTX1080 Ti board takes only less than 48 seconds while the INTEL i7 quad-core CPU requires 15 minutes and 42 seconds. A CUDA core is most commonly referring to the single-precision floating point units in an SM (streaming multiprocessor). A CUDA core can initiate one single precision floating point instruction per clock cycle. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing. The GPU parallel computer is based on SIMD ( single instruction, multiple data) computing.The first GPU for neural networks was used by Kyoung-Su Oh, et al. for image processing published in 2004 (1). A minimum GPU parallel computer is composed of a CPU board and a GPU board. This book contains the important issue on which CPU/GPU board you should buy and also illustrates how to integrate them in a single box by considering the heat problem. The power consumption of GPU is so large that we should take care of the temperature and heat from the GPU board in the single box. Our goal is to have the faster parallel computer with lower power dissipation. Software installation is another critical issue for machine learning in Python. Two operating system examples including Ubuntu16.04 and Windows 10 system will be described. This book shows how to install CUDA and cudnnlib in two operating systems. Three frameworks including pytorch, keras, and chainer for machine learning on CUDA and cudnnlib will be introduced. Matching problems between operating system (Ubuntu, Windows 10), library (CUDA, cudnnlib), and machine learning framework (pytorch, keras, chainer) are discussed. The paper entitled "GPU" and "open source software" play a key role for advancing deep learning was published in Science (eLetter, July 20 2017)http://science.sciencemag.org/content/357/6346/16/tab-e-letters

Advances in Parallel Computing Algorithms, Tools and Paradigms

D.J. Hemanth,T.N. Nguyen,J. Indumathi
Advances in Parallel Computing Algorithms Tools and Paradigms Book PDF

Author : D.J. Hemanth,T.N. Nguyen,J. Indumathi
Publisher : IOS Press
Page : 670 pages
File Size : 42,8 Mb
Release : 2022-11-23
Category : Computers
ISBN : 9781643683157

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Advances in Parallel Computing Algorithms, Tools and Paradigms by D.J. Hemanth,T.N. Nguyen,J. Indumathi Pdf

Recent developments in parallel computing for various fields of application are providing improved solutions for handling data. These newer, innovative ideas offer the technical support necessary to enhance intellectual decisions, while also dealing more efficiently with the huge volumes of data currently involved. This book presents the proceedings of ICAPTA 2022, the International Conference on Advances in Parallel Computing Technologies and Applications, hosted as a virtual conference from Bangalore, India, on 27 and 28 January 2022. The aim of the conference was to provide a forum for the sharing of knowledge about various aspects of parallel computing in communications systems and networking, including cloud and virtualization solutions, management technologies and vertical application areas. The conference also provided a premier platform for scientists, researchers, practitioners and academicians to present and discuss their most recent innovations, trends and concerns, as well as the practical challenges encountered in this field. More than 300 submissions were received for the conference, from which the 91 full-length papers presented here were accepted after review by a panel of subject experts. Topics covered include parallel computing in communication, machine learning intelligence for parallel computing and parallel computing for software services in theoretical and practical aspects. Providing an overview of recent developments in the field, the book will be of interest to all those whose work involves the use of parallel computing technologies.

Adaptive and Natural Computing Algorithms

Mikko Kolehmainen,Ville Kolehmainen,Pekka Toivanen,Bartlomiej Beliczynski
Adaptive and Natural Computing Algorithms Book PDF

Author : Mikko Kolehmainen,Ville Kolehmainen,Pekka Toivanen,Bartlomiej Beliczynski
Publisher : Springer Science & Business Media
Page : 645 pages
File Size : 49,8 Mb
Release : 2009-10-15
Category : Computers
ISBN : 9783642049200

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Adaptive and Natural Computing Algorithms by Mikko Kolehmainen,Ville Kolehmainen,Pekka Toivanen,Bartlomiej Beliczynski Pdf

This book constitutes the thoroughly refereed post-proceedings of the 9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009, held in Kuopio, Finland, in April 2009. The 63 revised full papers presented were carefully reviewed and selected from a total of 112 submissions. The papers are organized in topical sections on neutral networks, evolutionary computation, learning, soft computing, bioinformatics as well as applications.