Neural Network Architectures Examples Using Matlab

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Neural Network Architectures. Examples Using MATLAB

Author : J. Smith
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781544133317
Total Pages : 0 pages
Book Rating : 4.1/5 (333 download)

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Book Synopsis Neural Network Architectures. Examples Using MATLAB by : J. Smith

Download or read book Neural Network Architectures. Examples Using MATLAB written by J. Smith and published by Createspace Independent Publishing Platform. This book was released on 2017-02-26 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB has the tool Neural Network 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, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the following: - Deep learning, including convolutional neural networks and autoencoders - Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) - Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) - Unsupervised learning algorithms, including self-organizing maps and competitive layers - Apps for data-fitting, pattern recognition, and clustering - Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance - Simulink(R) blocks for building and evaluating neural networks and for control systems applications Neural networks are composed of simple elements operating in parallel. These elements are inspired by biological nervous systems. As in nature, the connections between elements largely determine the network function. You can train a neural network to perform a particular function by adjusting the values of the connections (weights) between elements.

Manual for the implementation of neural networks in MATLAB

Author : Michael Kuhn
Publisher : GRIN Verlag
ISBN 13 : 3638445518
Total Pages : 45 pages
Book Rating : 4.6/5 (384 download)

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Book Synopsis Manual for the implementation of neural networks in MATLAB by : Michael Kuhn

Download or read book Manual for the implementation of neural networks in MATLAB written by Michael Kuhn and published by GRIN Verlag. This book was released on 2005-12-05 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bachelor Thesis from the year 2005 in the subject Business economics - Information Management, grade: 2,0, Neisse University Görlitz (Neisse University), language: English, abstract: This bachelor thesis presents a manual about the implementation of neural networks in the software environment MATLAB. The thesis can be divided into four parts. After an introduction into the thesis, the theoretical background of neural networks and MATLAB is explained in two chapters. The third part is the description how to implement networks in a general way and with examples, too. The manual is created for the “Master Course of Computer Studies” at the University of Applied Science Zittau/Görlitz. Due to the fact, that this manual is a bachelor thesis just a small theoretical and practical overview about neural networks can be given.

Deep Learning and Dynamic Neural Networks With Matlab

Author : Perez C.
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781974063505
Total Pages : 166 pages
Book Rating : 4.0/5 (635 download)

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Book Synopsis Deep Learning and Dynamic Neural Networks With Matlab by : Perez C.

Download or read book Deep Learning and Dynamic Neural Networks With Matlab written by Perez C. and published by Createspace Independent Publishing Platform. This book was released on 2017-07-31 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is a branch of machine learning that teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model. Deep learning is especially suited for image recognition, which is important for solving problems such as facial recognition, motion detection, and many advanced driver assistance technologies such as autonomous driving, lane detection, pedestrian detection, and autonomous parking. Neural Network Toolbox provides simple MATLAB commands for creating and interconnecting the layers of a deep neural network. Examples and pretrained networks make it easy to use MATLAB for deep learning, even without knowledge of advanced computer vision algorithms or neural networks. The Neural Network Toolbox software uses the network object to store all of the information that defines a neural network. After a neural network has been created, it needs to be configured and then trained. Configuration involves arranging the network so that it is compatible with the problem you want to solve, as defined by sample data. After the network has been configured, the adjustable network parameters (called weights and biases) need to be tuned, so that the network performance is optimized. This tuning process is referred to as training the network. Configuration and training require that the network be provided with example data. This topic shows how to format the data for presentation to the network. It also explains network configuration and the two forms of network training: incremental training and batch training. Neural networks can be classified into dynamic and static categories. Static (feedforward) networks have no feedback elements and contain no delays; the output is calculated directly from the input through feedforward connections. In dynamic networks, the output depends not only on the current input to the network, but also on the current or previous inputs, outputs, or states of the network. This book develops the following topics: - "Workflow for Neural Network Design" - "Neural Network Architectures" - "Deep Learning in MATLAB" - "Deep Network Using Autoencoders" - "Convolutional Neural Networks" - "Multilayer Neural Networks" - "Dynamic Neural Networks" - "Time Series Neural Networks" - "Multistep Neural Network Prediction"

MATLAB Deep Learning

Author : Phil Kim
Publisher : Apress
ISBN 13 : 1484228456
Total Pages : 162 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis MATLAB Deep Learning by : Phil Kim

Download or read book MATLAB Deep Learning written by Phil Kim and published by Apress. This book was released on 2017-06-15 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.

Neural Networks Theory and Examples with MATLAB

Author : C Perez
Publisher : Independently Published
ISBN 13 : 9781093618778
Total Pages : 206 pages
Book Rating : 4.6/5 (187 download)

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Book Synopsis Neural Networks Theory and Examples with MATLAB by : C Perez

Download or read book Neural Networks Theory and Examples with MATLAB written by C Perez and published by Independently Published. This book was released on 2019-04-11 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks theory is inspired from the natural neural network of human nervous system. Is possible define a neural network as a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.This book develops the architecture of the most important neural networks: Perceptron, ADALINE, Radial Basis, Hopfield, Probabilistic, Generalized regression and LVQ neural Networks. It also presents practical examples of the different architectures of neural networks.

Big Data and Deep Learning. Examples with MATLAB

Author : C. Perez
Publisher : CESAR PEREZ
ISBN 13 : 1716877563
Total Pages : 328 pages
Book Rating : 4.7/5 (168 download)

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Book Synopsis Big Data and Deep Learning. Examples with MATLAB by : C. Perez

Download or read book Big Data and Deep Learning. Examples with MATLAB written by C. Perez and published by CESAR PEREZ. This book was released on 2020-05-31 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today's technology, it's possible to analyze your data and get answers from it almost immediately - an effort that's slower and less efficient with more traditional business intelligence solutions. Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.Deep learning has been characterized as a buzzword, or a rebranding of neural networks. This book deeps in big data and deep learning techniques

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

Author : PEREZ C.
Publisher : CESAR PEREZ
ISBN 13 : 1974082040
Total Pages : 78 pages
Book Rating : 4.9/5 (74 download)

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

Download or read book ADVANCED TOPICS IN NEURAL NETWORKS WITH MATLAB. PARALLEL COMPUTING, OPTIMIZE AND TRAINING written by PEREZ C. and published by CESAR PEREZ. This book was released on 2023-12-13 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Neural Network Design

Author : Martin T. Hagan
Publisher :
ISBN 13 : 9789812403766
Total Pages : pages
Book Rating : 4.4/5 (37 download)

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Book Synopsis Neural Network Design by : Martin T. Hagan

Download or read book Neural Network Design written by Martin T. Hagan and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

DEEP LEARNING with MATLAB. NEURAL NETWORKS by EXAMPLES

Author : Cesar Perez Lopez
Publisher : CESAR PEREZ
ISBN 13 : 1716584841
Total Pages : 154 pages
Book Rating : 4.7/5 (165 download)

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Book Synopsis DEEP LEARNING with MATLAB. NEURAL NETWORKS by EXAMPLES by : Cesar Perez Lopez

Download or read book DEEP LEARNING with MATLAB. NEURAL NETWORKS by EXAMPLES written by Cesar Perez Lopez and published by CESAR PEREZ. This book was released on 2020-09-13 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

PREDICTIVE ANALYTICS with NEURAL NETWORKS Using MATLAB

Author : Cesar Perez Lopez
Publisher : CESAR PEREZ
ISBN 13 : 1716601568
Total Pages : 239 pages
Book Rating : 4.7/5 (166 download)

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Book Synopsis PREDICTIVE ANALYTICS with NEURAL NETWORKS Using MATLAB by : Cesar Perez Lopez

Download or read book PREDICTIVE ANALYTICS with NEURAL NETWORKS Using MATLAB written by Cesar Perez Lopez and published by CESAR PEREZ. This book was released on 2020-09-06 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. Different work fields with neural networks and predictive analytics techniques are listed below: The multilayer perceptron (MLP), A radial basis function (RBF), Support vector machines (SVM), Fit regression models with neural networks, Time series neural networks, Hopfield and linear neural networks, Generalized regression and LVQ neural networks, Adaptative linear filters and non linear problems

TIME SERIES FORECASTING USING NEURAL NETWORKS. EXAMPLES WITH MATLAB

Author : Cesar Perez Lopez
Publisher : CESAR PEREZ
ISBN 13 :
Total Pages : 283 pages
Book Rating : 4./5 ( download)

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Book Synopsis TIME SERIES FORECASTING USING NEURAL NETWORKS. EXAMPLES WITH MATLAB by : Cesar Perez Lopez

Download or read book TIME SERIES FORECASTING USING NEURAL NETWORKS. EXAMPLES WITH MATLAB written by Cesar Perez Lopez and published by CESAR PEREZ. This book was released on with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB has the tool Deep Leraning Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, timeseries forecasting, and dynamic system modeling and control. Dynamic neural networks are good at timeseries prediction. You can use the Neural Net Time Series app to solve different kinds of time series problems It is generally best to start with the GUI, and then to use the GUI to automatically generate command line scripts. Before using either method, the first step is to define the problem by selecting a data set. Each GUI has access to many sample data sets that you can use to experiment with the toolbox. If you have a specific problem that you want to solve, you can load your own data into the workspace. With MATLAB is possibe to solve three different kinds of time series problems. In the first type of time series problem, you would like to predict future values of a time series y(t) from past values of that time series and past values of a second time series x(t). This form of prediction is called nonlinear autoregressive network with exogenous (external) input, or NARX. In the second type of time series problem, there is only one series involved. The future values of a time series y(t) are predicted only from past values of that series. This form of prediction is called nonlinear autoregressive, or NAR. The third time series problem is similar to the first type, in that two series are involved, an input series (predictors) x(t) and an output series (responses) y(t). Here you want to predict values of y(t) from previous values of x(t), but without knowledge of previous values of y(t). This book develops methods for time series forecasting using neural networks across MATLAB

Fundamentals of Neural Networks

Author : Fausett
Publisher : Prentice Hall
ISBN 13 : 9780133367690
Total Pages : 300 pages
Book Rating : 4.3/5 (676 download)

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Book Synopsis Fundamentals of Neural Networks by : Fausett

Download or read book Fundamentals of Neural Networks written by Fausett and published by Prentice Hall. This book was released on 1994 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt:

ARTIFICIAL INTELLIGENCE with MATLAB. DEEP LEARNING ARCHITECTURES

Author : J Abbel
Publisher :
ISBN 13 :
Total Pages : 200 pages
Book Rating : 4.6/5 (494 download)

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Book Synopsis ARTIFICIAL INTELLIGENCE with MATLAB. DEEP LEARNING ARCHITECTURES by : J Abbel

Download or read book ARTIFICIAL INTELLIGENCE with MATLAB. DEEP LEARNING ARCHITECTURES written by J Abbel and published by . This book was released on 2020-05-28 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB enables the design of artificial intelligence models through three essential pillars: Machine Learning, Deep Learning and Data Science. Using MATLAB, engineers and other experts have deployed thousands of machine learning applications. Automated machine learning (AutoML) generate automatically functionalities from training data and optimize models using hyperparameter fitting techniques such as Bayesian optimization. Use specialized functionalities extraction techniques, such as wavelet dispersion for signal or image data, and functionalities selection techniques, such as neighbor component analysis (NCA) or sequential functionalities selection. Deep Learning is a subset of machine learning based on artificial neural networks. The process of this learning is called deep because this network structure consists of having multiple inputs, outputs and hidden layers. Each layer contains units that transform the input data into information, and in this way, the next layer can use it for a certain predictive task. In this way, a machine can learn through its own data processing. MATLAB has the tool Neural Network Toolbox (Deep Leraning toolbox fron release 18) 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. This book develops the neural network architectures used in Deep Learning.

Predictive Analytics With Neural Networks Using Matlab

Author : J. Smith
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781544169613
Total Pages : 242 pages
Book Rating : 4.1/5 (696 download)

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Book Synopsis Predictive Analytics With Neural Networks Using Matlab by : J. Smith

Download or read book Predictive Analytics With Neural Networks Using Matlab written by J. Smith and published by Createspace Independent Publishing Platform. This book was released on 2017-02-27 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement. Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, child protection, pharmaceuticals, capacity planning and other fields. One of the best-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. Neural networks are nonlinear sophisticated modeling techniques that are able to model complex functions. They can be applied to problems of prediction, classification or control in a wide spectrum of fields such as finance, cognitive psychology/neuroscience, medicine, engineering, and physics. Neural networks are used when the exact nature of the relationship between inputs and output is not known. A key feature of neural networks is that they learn the relationship between inputs and output through training. There are three types of training used by different neural networks: supervised and unsupervised training and reinforcement learning, with supervised being the most common one. Some examples of neural network training techniques are backpropagation, quick propagation, conjugate gradient descent, projection operator, Delta-Bar-Delta etc. Some unsupervised network architectures are multilayer perceptrons, Kohonen networks, Hopfield networks, etc. Different work fields with neural networks and predictive analytics techniques are developed in this book: -The multilayer perceptron (MLP) -A radial basis function (RBF) i -Fit regression models with neural networks. -Time series neural networks. Modeling and prediction with NARX and time delay networks. -Hopfield and linear neural networks. -Generalized regression and LVQ neural networks. -Adaptative linear filters and non linear problems. Used for linear and nonlinear prediction

Machine and Deep Learning Using MATLAB

Author : Kamal I. M. Al-Malah
Publisher : John Wiley & Sons
ISBN 13 : 139420910X
Total Pages : 596 pages
Book Rating : 4.3/5 (942 download)

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Book Synopsis Machine and Deep Learning Using MATLAB by : Kamal I. M. Al-Malah

Download or read book Machine and Deep Learning Using MATLAB written by Kamal I. M. Al-Malah and published by John Wiley & Sons. This book was released on 2023-10-12 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: MACHINE AND DEEP LEARNING In-depth resource covering machine and deep learning methods using MATLAB tools and algorithms, providing insights and algorithmic decision-making processes Machine and Deep Learning Using MATLAB introduces early career professionals to the power of MATLAB to explore machine and deep learning applications by explaining the relevant MATLAB tool or app and how it is used for a given method or a collection of methods. Its properties, in terms of input and output arguments, are explained, the limitations or applicability is indicated via an accompanied text or a table, and a complete running example is shown with all needed MATLAB command prompt code. The text also presents the results, in the form of figures or tables, in parallel with the given MATLAB code, and the MATLAB written code can be later used as a template for trying to solve new cases or datasets. Throughout, the text features worked examples in each chapter for self-study with an accompanying website providing solutions and coding samples. Highlighted notes draw the attention of the user to critical points or issues. Readers will also find information on: Numeric data acquisition and analysis in the form of applying computational algorithms to predict the numeric data patterns (clustering or unsupervised learning) Relationships between predictors and response variable (supervised), categorically sub-divided into classification (discrete response) and regression (continuous response) Image acquisition and analysis in the form of applying one of neural networks, and estimating net accuracy, net loss, and/or RMSE for the successive training, validation, and testing steps Retraining and creation for image labeling, object identification, regression classification, and text recognition Machine and Deep Learning Using MATLAB is a useful and highly comprehensive resource on the subject for professionals, advanced students, and researchers who have some familiarity with MATLAB and are situated in engineering and scientific fields, who wish to gain mastery over the software and its numerous applications.

Efficient Processing of Deep Neural Networks

Author : Vivienne Sze
Publisher : Springer Nature
ISBN 13 : 3031017668
Total Pages : 254 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Efficient Processing of Deep Neural Networks by : Vivienne Sze

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Turing’s Connectionism

Author : Christof Teuscher
Publisher : Springer Science & Business Media
ISBN 13 : 1447101618
Total Pages : 215 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Turing’s Connectionism by : Christof Teuscher

Download or read book Turing’s Connectionism written by Christof Teuscher and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: Christof Teuscher revives, analyzes, and simulates Turing's ideas, applying them to different types of problems, and building and training Turing's machines using evolutionary algorithms. In a little known paper entitled 'Intelligent Machinery' Turing investigated connectionist networks, but his work was dismissed as a 'schoolboy essay'and it was left unpublished until 1968, 14 years after his death. This is not a book about today's (classical) neural networks, but about the neuron network-like structures proposed by Turing. One of its novel features is that it actually goes beyond Turing's ideas by proposing new machines. The book also contains a Foreward by B. Jack Copeland and D. Proudfoot.