Download or read book Big Data Analytics with MATLAB written by Marvin L. and published by Createspace Independent Publishing Platform. This book was released on 2016-10-24 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits. The primary goal of big data analytics is to help companies make more informed business decisions by enabling data scientists, predictive modelers and other analytics professionals to analyze large volumes of transaction data, as well as other forms of data that may be untapped by conventional business intelligence (BI) programs. That could include Web server logs and Internet clickstream data, social media content and social network activity reports, text from customer emails and survey responses, mobile-phone call detail records and machine data captured by sensors connected to the Internet of Things.

Download or read book Big Data Analytics written by C. Perez and published by CESAR PEREZ. This book was released on 2020-05-31 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 (Deep Learning Toolbox for version 18) . This book develops the work capabilities of MATLAB with Neural Networks and Big Data.

Download or read book Big Data Analytics With Matlab written by L. Abell and published by Createspace Independent Publishing Platform. This book was released on 2017-09-09 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term "big data," businesses were using basic analytics (essentially numbers in a spreadsheet that were manually examined) to uncover insights and trends. The new benefits that big data analytics brings to the table, however, are speed and efficiency. Whereas a few years ago a business would have gathered information, run analytics and unearthed information that could be used for future decisions, today that business can identify insights for immediate decisions. The ability to work faster - and stay agile - gives organizations a competitive edge they didn't have before. Large data sets can be in the form of large files that do not fit into available memory or files that take a long time to process. A large data set also can be a collection of numerous small files. There is no single approach to working with large data sets, so MATLAB includes a number of tools for accessing and processing large data. Begin by creating a datastore that can access small portions of the data at a time. You can use the datastore to manage incremental import of the data. To analyze the data using common MATLAB functions, such as mean and histogram, create a tall array on top of the datastore. 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. MATLAB has the tools to work with large datasets and apply the necessary data analysis techniques. This book develops the work with MATLAB for the processing, exploration and visualization of data.

Download or read book DATA MINING and BIG DATA ANALYTICS with NEURAL NETWORKS Using MATLAB written by C Perez and published by Independently Published. This book was released on 2019-05-22 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: The availability of large volumes of data (Big Data) and the generalized use of computer tools has transformed research and data analysis, orienting it towards certain specialized techniques encompassed under the generic name of Analytics (Big Data Analytics) that includes Multivariate Data Analysis (MDA), Data Mining and other Business Intelligence techniques.Data Mining can be defined as a process of discovering new and significant relationships, patterns and trends when examining large amounts of data. The techniques of Data Mining pursue the automatic discovery of the knowledge contained in the information stored in an orderly manner in large databases. These techniques aim to discover patterns, profiles and trends through the analysis of data using advanced statistical techniques of multivariate data analysis.The goal is to allow the researcher-analyst to find a useful solution to the problem raised through a better understanding of the existing data.Data Mining uses two types of techniques: predictive techniques, which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques, which finds hidden patterns or intrinsic structures in input data.

Download or read book Big Data Analytics with Neural Networks 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: 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.

Download or read book Big Data Analytics Using Matlab written by L. Abell and published by Createspace Independent Publishing Platform. This book was released on 2017-09-04 with total page 438 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. 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

Download or read book Big Data Analytics written by C. Perez and published by CESAR PEREZ. This book was released on 2020-05-31 with total page 389 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. MATLAB has the tool Neural Network Toolbox (Deep Learning Toolbox from version 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.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 Big Data tools (Parallel Computing Toolbox). 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. his book develops cluster analysis and pattern recognition

Download or read book Big Data Analytics With Matlab written by L. Marvin and published by Createspace Independent Publishing Platform. This book was released on 2017-09-09 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB has the tools to work with large datasets and apply the necessary data analysis techniques. This book develops the work with predictive linear and nonlinear models. 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. Predictive models describe the relationship between a response (output) variable, and one or more predictor (input) variables. Statistics and Machine Learning Toolbox allows you to fit linear, generalized linear, and nonlinear regression models, including stepwise models and mixed-effects models. Once you fit a model, you can use it to predict or simulate responses, assess the model fit using hypothesis tests, or use plots to visualize diagnostics, residuals, and interaction effects. Statistics and Machine Learning Toolbox also provides nonparametric regression methods to accommodate more complex regression curves without specifying the relationship between the response and the predictors with a predetermined regression function. You can predict responses for new data using the trained model. Gaussian process regression models also enable you to compute prediction intervals.

Download or read book DATA MINING BIG DATA ANALYTICS and DEEP LEARNING with MATLAB written by C Perez and published by . This book was released on 2019-05-24 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. In a simple case, there might be two sets of neurons: ones that receive an input signal and ones that send an output signal. When the input layer receives an input it passes on a modified version of the input to the next layer. In a deep network, there are many layers between the input and output (and the layers are not made of neurons but it can help to think of it that way), allowing the algorithm to use multiple processing layers, composed of multiple linear and non-linear transformations.Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations are better than others at simplifying the learning task (e.g., face recognition or facial expression recognition). One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction. Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain. 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.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.

Download or read book DATA MINING BIG DATA ANALYTICS and MACHINE LEARNING with NEURAL NETWORKS Using MATLAB written by C Perez and published by Independently Published. This book was released on 2019-05-23 with total page 388 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.The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term "big data," businesses were using basic analytics (essentially numbers in a spreadsheet that were manually examined) to uncover insights and trends.Data Mining can be defined as a process of discovering new and significant relationships, patterns and trends when examining large amounts of data. The techniques of Data Mining pursue the automatic discovery of the knowledge contained in the information stored in an orderly manner in large databases. These techniques aim to discover patterns, profiles and trends through the analysis of data using advanced statistical techniques of multivariate data analysis.The goal is to allow the researcher-analyst to find a useful solution to the problem raised through a better understanding of the existing data.Data Mining uses two types of techniques: predictive techniques, which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques, which finds hidden patterns or intrinsic structures in input data.

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.

Download or read book Data Driven Modeling Scientific Computation written by Jose Nathan Kutz and published by . This book was released on 2013-08-08 with total page 657 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combining scientific computing methods and algorithms with modern data analysis techniques, including basic applications of compressive sensing and machine learning, this book develops techniques that allow for the integration of the dynamics of complex systems and big data. MATLAB is used throughout for mathematical solution strategies.

Download or read book Big Data Analytics With Matlab written by L. Marvin and published by Createspace Independent Publishing Platform. This book was released on 2017-09-09 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB has the tools to work with large datasets and apply the necessary data analysis techniques. This book develops the work with parametric regression analysis techniques. 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. Regression models describe the relationship between a response (output) variable, and one or more predictor (input) variables. Statistics and Machine Learning Toolbox allows you to fit linear, generalized linear, and nonlinear regression models, including stepwise models and mixed-effects models. Once you fit a model, you can use it to predict or simulate responses, assess the model fit using hypothesis tests, or use plots to visualize diagnostics, residuals, and interaction effects. Statistics and Machine Learning Toolbox also provides nonparametric regression methods to accommodate more complex regression curves without specifying the relationship between the response and the predictors with a predetermined regression function. You can predict responses for new data using the trained model. Gaussian process regression models also enable you to compute prediction intervals.

Download or read book Big Data Analytics With Matlab written by L. Marvin and published by Createspace Independent Publishing Platform. This book was released on 2017-09-10 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB has the tools to work with large datasets and apply the necessary data analysis techniques. This book develops the work with nonlinear regression analysis techniques. 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. Nonlinear Regression models describe the nonlinear relationship between a response (output) variable, and one or more predictor (input) variables. Statistics and Machine Learning Toolbox allows you to fit linear, generalized linear, and nonlinear regression models, including stepwise models and mixed-effects models. Once you fit a model, you can use it to predict or simulate responses, assess the model fit using hypothesis tests, or use plots to visualize diagnostics, residuals, and interaction effects. Statistics and Machine Learning Toolbox also provides nonparametric regression methods to accommodate more complex regression curves without specifying the relationship between the response and the predictors with a predetermined regression function. You can predict responses for new data using the trained model. Gaussian process regression models also enable you to compute prediction intervals.

Download or read book MATLAB written by Antonio Siciliano and published by World Scientific. This book was released on 2008 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Windows of the Desktop; A Preliminary Approach to Data and M-Files; Scripts and Functions as M-Files; Numerical Arrays; Other Types of Arrays; The Figure Window for Graphics Objects; Plot 2-D and Image; Flow Control; Appendices: MATLAB Functions Categories; MATLAB Functions and Objects Properties; Operators List; A Table of Special Ascii Codes.

Download or read book Big Data with MATLAB written by C. Perez and published by Independently Published. This book was released on 2019-04-03 with total page 388 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.The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term "big data," businesses were using basic analytics (essentially numbers in a spreadsheet that were manually examined) to uncover insights and trends.There's no single technology that encompasses big data analytics. Of course, there's advanced analytics that can be applied to big data, but in reality several types of technology work together to help you get the most value from your informationIn MATLAB(R) you can access remote data using datastore objects. You can create a datastore to work with data stored in remote locations, such as cloud storage using Amazon S3(TM) (Simple Storage Service) and Hadoop(R) Distributed File System (HDFS(TM)). Use the datastore to examine part of your data from your desktop version of MATLAB. Then, after prototyping your code locally, you can scale up to a cluster or cloud. Scaling up improves execution efficiency as it is more efficient to run large calculations in the same location as the data.Tall arrays provide a way to work with data backed by a datastore that can have millions or billions of rows. You can create tall numeric arrays, cell arrays, categoricals, strings, datetimes, durations, or calendar durations, and you can use any of these tall types as variables in a tall table or tall timetable. Many operations and functions work the same way with tall arrays as they do with in-memory MATLAB(R) arrays, but most results are evaluated only when you request them explicitly using gather. MATLAB automatically optimizes the queued calculations by minimizing the number of passes through the data.As the number and type of data acquisition devices grows annually, the sheer size and rate of data being collected is rapidly expanding. These big data sets can contain gigabytes or terabytes of data, and can grow on the order of megabytes or gigabytes per day. While the collection of this information presents opportunities for insight, it also presents many challenges. Most algorithms are not designed to process big data sets in a reasonable amount of time or with a reasonable amount of memory. MapReduce allows you to meet many of these challenges to gain important insights from large data sets.Parallel Computing Toolbox can immediately speed up your tall array calculations by using the full processing power of multicore computers to execute applications with a parallel pool of workers

Download or read book Big Data Analytics With Matlab written by L. Abell and published by Createspace Independent Publishing Platform. This book was released on 2017-09-09 with total page 208 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. MATLAB has the tools to work with large datasets and apply the necessary data analysis techniques. This book develops the work with MATLAB for model and fit data to probabuility distributions. Fit probability distributions to sample data, evaluate probability functions such as pdf and cdf, calculate summary statistics such as mean and median, visualize sample data, generate random numbers, and more. Work with probability distributions using probability distribution objects, command line functions, or interactive apps. Probability distributions are theoretical distributions based on assumptions about a source population. The distributions assign probability to the event that a random variable has a specific, discrete value, or falls within a specified range of continuous values. MATLAB Statistics and Machine Learning Toolbox offers several ways to work with probability distributions. Use Probability Distribution Objects to fit a probability distribution object to sample data, or to create a probability distribution object with specified parameter values. Use Probability Distribution Functions to work with data input from matrices, tables, and dataset arrays. Use Probability Distribution Apps and User Interfaces to interactively fit, explore, and generate random numbers from probability distributions. Available apps and user interfaces include: The Distribution Fitter app The Probability Distribution Function user interface The Random Number Generation user interface (randtool)