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Big Data And Deep Learning Examples With Matlab

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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

Big Data And Deep Learning Examples With Matlab

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Author : PEREZ. C. PEREZ
language : en
Publisher:
Release Date : 2020

Big Data And Deep Learning Examples With Matlab written by PEREZ. C. PEREZ 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.

Matlab Deep Learning

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Author : Phil Kim
language : en
Publisher: Apress
Release Date : 2017-06-15

Matlab Deep Learning written by Phil Kim and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-15 with Computers categories.

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.

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.

Big Data Analytics

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

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

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.

Matlab Machine Learning Recipes

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Author : Michael Paluszek
language : en
Publisher: Apress
Release Date : 2019-01-31

Matlab Machine Learning Recipes written by Michael Paluszek and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-31 with Computers categories.

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. What you'll learn:How to write code for machine learning, adaptive control and estimation using MATLAB How these three areas complement each other How these three areas are needed for robust machine learning applications How to use MATLAB graphics and visualization tools for machine learning How to code real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers, data scientists and students wanting a comprehensive and code cookbook rich in examples on machine learning using MATLAB.

Machine Learning Models And Algorithms For Big Data Classification

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Author : Shan Suthaharan
language : en
Publisher: Springer
Release Date : 2015-10-20

Machine Learning Models And Algorithms For Big Data Classification written by Shan Suthaharan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-20 with Business & Economics categories.

This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.

Matlab Machine Learning

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Author : Michael Paluszek
language : en
Publisher: Apress
Release Date : 2016-12-28

Matlab Machine Learning written by Michael Paluszek and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-28 with Computers categories.

This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning. The book reviews commercially available packages for machine learning and shows how they fit into the field. The book then shows how MATLAB can be used to solve machine learning problems and how MATLAB graphics can enhance the programmer’s understanding of the results and help users of their software grasp the results. Machine Learning can be very mathematical. The mathematics for each area is introduced in a clear and concise form so that even casual readers can understand the math. Readers from all areas of engineering will see connections to what they know and will learn new technology. The book then provides complete solutions in MATLAB for several important problems in machine learning including face identification, autonomous driving, and data classification. Full source code is provided for all of the examples and applications in the book. What you'll learn: An overview of the field of machine learning Commercial and open source packages in MATLAB How to use MATLAB for programming and building machine learning applications MATLAB graphics for machine learning Practical real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers and engineering students wanting a comprehensive and practical introduction to machine learning.

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.

Big Data And Deep Learning Examples With Matlab

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