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Predictive Analytics With Neural Networks Using Matlab

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Predictive Analytics With Neural Networks Using Matlab

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Author : Cesar Perez Lopez
language : en
Publisher: CESAR PEREZ
Release Date : 2020-09-06

Predictive Analytics With Neural Networks Using Matlab 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 2020-09-06 with Computers categories.

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

Predictive Analytics With Neural Networks Using Matlab

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Author : Perez Lopez Cesar Perez Lopez
language : en
Publisher:
Release Date : 2020

Predictive Analytics With Neural Networks Using Matlab written by Perez Lopez Cesar Perez Lopez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.

Predictive Analytics With Neural Networks Using Matlab

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Author : J. Smith
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-02-27

Predictive Analytics 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 categories.

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

Big Data Analytics With Neural Networks Using Matlab

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Author : J. Smith
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-02-26

Big Data Analytics 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 2017-02-26 with Big data categories.

Big data analytics is the process of collecting, organizing and analyzing large sets of data (called big data) to discover patterns and other useful information. Big data analytics can help organizations to better understand the information contained within the data and will also help identify the data that is most important to the business and future business decisions. Analysts working with big data basically want the knowledge that comes from analyzing the data. To analyze such a large volume of data, big data analytics is typically performed using specialized software tools and applications for predictive analytics, data mining, text mining, forecasting and data optimization. Collectively these processes are separate but highly integrated functions of high-performance analytics. Using big data tools and software enables an organization to process extremely large volumes of data that a business has collected to determine which data is relevant and can be analyzed to drive better business decisions in the future. Among all these tools highlights MATLAB. MATLAB implements various toolboxes for working on big data analytics, such as Statistics Toolbox and Neural Network Toolbox. This book develops Big Data Analytics applications using MATLAB Neural Network Toolboox. 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.

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

Predictive Analytics With Matlab Regression And Neural Networks

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Author : J. Smith
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-04-18

Predictive Analytics With Matlab Regression And Neural Networks 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 2017-04-18 with categories.

Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. This books develops the more important predictive models like Regression Models, Generalized Regression Models, Discrete Choice Models, Logit and Probit Models, Support Vector Machine Regression, Gaussian Process Regresion, Regression Trees, Regression Models with Neural Networks and Neural Networks Time Series Prediction.

Big Data Analytics

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Author : C. Perez
language : en
Publisher: CESAR PEREZ
Release Date : 2020-05-31

Big Data Analytics written by C. Perez and has been published by CESAR PEREZ this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-31 with Computers categories.

Predictive Analytics With Matlab Machine Learning Techniques

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Author : J. Smith
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-05

Predictive Analytics With Matlab Machine Learning Techniques 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 2017-05 with categories.

Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. This books develops the important predictive models like Support Vector Machine, Nearest Neighbors. KNN Classifiers, Support Vector Machine Regression, Gaussian Process Regresion, Classification and Regression Trees, Regression Models with Neural Networks, Neural Networks Time Series Prediction and Classification with Naive Bayes.

Big Data Analytics Using Matlab

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Author : L. Abell
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-09-04

Big Data Analytics Using 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 2017-09-04 with categories.

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. A key tool in big data analytics are the neural networks. 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 P2 GPU instances) with MATLAB 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

Big Data And Deep Learning Examples With Matlab

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Author : C. Perez
language : en
Publisher: CESAR PEREZ
Release Date : 2020-05-31

Big Data And Deep Learning Examples With Matlab written by C. Perez and has been published by CESAR PEREZ this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-31 with Computers categories.

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

Predictive Analytics With Neural Networks Using Matlab

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