Neural Network Toolbox For Use With Matlab


Neural Network Toolbox For Use With Matlab
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Neural Network Toolbox For Use With Matlab


Neural Network Toolbox For Use With Matlab
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Author : Howard B. Demuth
language : en
Publisher:
Release Date : 2006

Neural Network Toolbox For Use With Matlab written by Howard B. Demuth and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.




Matlab Neural Network Toolbox User S Guide


Matlab Neural Network Toolbox User S Guide
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Author : Howard B. Demuth
language : en
Publisher:
Release Date : 1992

Matlab Neural Network Toolbox User S Guide written by Howard B. Demuth and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with MATLAB categories.




Neural Network Toolbox


Neural Network Toolbox
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Author : Howard Demuth
language : en
Publisher:
Release Date : 2007

Neural Network Toolbox written by Howard Demuth and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.




Matlab For Machine Learning


Matlab For Machine Learning
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Author : Giuseppe Ciaburro
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-08-28

Matlab For Machine Learning written by Giuseppe Ciaburro 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 2017-08-28 with Computers categories.


Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.



Advenced Neural Networks With Matlab


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



Manual For The Implementation Of Neural Networks In Matlab


Manual For The Implementation Of Neural Networks In Matlab
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Author : Michael Kuhn
language : en
Publisher: GRIN Verlag
Release Date : 2005-12-05

Manual For The Implementation Of Neural Networks In Matlab written by Michael Kuhn and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-05 with Business & Economics categories.


Bachelor Thesis from the year 2005 in the subject Information Management, grade: 2,0, Neisse University Görlitz (Neisse University), 45 entries in the bibliography, 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.



Computer Neural Networks On Matlab


Computer Neural Networks On Matlab
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Author : Daniel Okoh
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-10-07

Computer Neural Networks On Matlab written by Daniel Okoh 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 2016-10-07 with categories.


Computer neural networks are a branch of artificial intelligence, inspired to behave in a manner similar to the human brain; they are trained and they learn from their training. Computer neural networks have a wide variety of applications, mostly hinged around modelling, forecasting, and general predictions. This book illustrates how to use computer neural networks on MATLAB in very simple and elegant manner. The language of the book is elementary as it is meant for beginners, readers are notassumed to have previous skills on the subject. Projects, in varying degrees, have been used to make sure that readers get a practical and hands-on experience on the subject. The book is meant for you if you want to get a quick start with the practical use of computer neural networks on MATLAB without the boredom associated with a lengthy theoretical write-up.



Machine Learning With Neural Networks Using Matlab


Machine Learning With Neural Networks Using Matlab
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Author : J. Smith
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-02-27

Machine Learning With Neural Networks Using Matlab written by J. Smith 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 2017-02-27 with MATLAB. categories.


Machine Learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data. 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, dynamic system modeling and control and most machine learning techniques. 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



Deep Learning With Matlab Neural Networks By Examples


Deep Learning With Matlab Neural Networks By Examples
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Author : Cesar Perez Lopez
language : en
Publisher: CESAR PEREZ
Release Date : 2020-09-13

Deep Learning With Matlab Neural Networks By Examples written by Cesar Perez Lopez and has been published by CESAR PEREZ this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-13 with Computers categories.


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.



Neural Network Architectures Examples Using Matlab


Neural Network Architectures Examples Using Matlab
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Author : J. Smith
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-02-26

Neural Network Architectures Examples Using Matlab written by J. Smith 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 2017-02-26 with Computer architecture categories.


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.