Big Data Analytics
Big Data Analytics

Author: C. Perez

Publisher: CESAR PEREZ

Published: 2020-05-31

Total Pages: 389

ISBN-13: 1716876869

DOWNLOAD EBOOK

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

Big Data Analytics
Big Data Analytics

Author: C. Perez

Publisher: CESAR PEREZ

Published: 2020-05-31

Total Pages: 322

ISBN-13: 1716877423

DOWNLOAD EBOOK

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.

Introduction to Pattern Recognition
Introduction to Pattern Recognition

Author: Sergios Theodoridis

Publisher: Academic Press

Published: 2010-03-03

Total Pages: 233

ISBN-13: 0080922759

DOWNLOAD EBOOK

Introduction to Pattern Recognition: A Matlab Approach is an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition. It includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal processing/analyisis, and computer vision. - Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition, Fourth Edition - Solved examples in Matlab, including real-life data sets in imaging and audio recognition - Available separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)

Production Planning and Control in Semiconductor Manufacturing
Production Planning and Control in Semiconductor Manufacturing

Author: Tin-Chih Toly Chen

Publisher: Springer Nature

Published: 2022-09-19

Total Pages: 106

ISBN-13: 3031140656

DOWNLOAD EBOOK

This book systematically analyzes the applicability of big data analytics and Industry 4.0 from the perspective of semiconductor manufacturing management. It reports in real examples and presents case studies as supporting evidence. In recent years, technologies of big data analytics and Industry 4.0 have been frequently applied to the management of semiconductor manufacturing. However, related research results are mostly scattered in various journal issues or conference proceedings, and there is an urgent need for a systematic integration of these results. In addition, many related discussions have placed too much emphasis on the theoretical framework of information systems rather than on the needs of semiconductor manufacturing management. This book addresses these issues.

Clustering Algorithms
Clustering Algorithms

Author: John A. Hartigan

Publisher: John Wiley & Sons

Published: 1975

Total Pages: 374

ISBN-13:

DOWNLOAD EBOOK

Shows how Galileo, Newton, and Einstein tried to explain gravity. Discusses the concept of microgravity and NASA's research on gravity and microgravity.

Multivariate Observations
Multivariate Observations

Author: George A. F. Seber

Publisher: John Wiley & Sons

Published: 2009-09-25

Total Pages: 718

ISBN-13: 0470317310

DOWNLOAD EBOOK

WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "In recent years many monographs have been published on specialized aspects of multivariate data-analysis–on cluster analysis, multidimensional scaling, correspondence analysis, developments of discriminant analysis, graphical methods, classification, and so on. This book is an attempt to review these newer methods together with the classical theory. . . . This one merits two cheers." –J. C. Gower, Department of Statistics Rothamsted Experimental Station, Harpenden, U.K. Review in Biometrics, June 1987 Multivariate Observations is a comprehensive sourcebook that treats data-oriented techniques as well as classical methods. Emphasis is on principles rather than mathematical detail, and coverage ranges from the practical problems of graphically representing high-dimensional data to the theoretical problems relating to matrices of random variables. Each chapter serves as a self-contained survey of a specific topic. The book includes many numerical examples and over 1,100 references.

Smart Technologies and Innovations in Design for Control of Technological Processes and Objects: Economy and Production
Smart Technologies and Innovations in Design for Control of Technological Processes and Objects: Economy and Production

Author: Denis B. Solovev

Publisher: Springer

Published: 2019-05-18

Total Pages: 807

ISBN-13: 3030185532

DOWNLOAD EBOOK

This book features selected papers presented at The International Science and Technology Conference “FarEastCon”, which took place on October 2–4, 2018 in Vladivostok, Russian Federation. The conference represents an informational platform for accumulating expert opinion on projects and initiatives aimed at the implementation of farsighted scientific research and development; it also allows scientific and practical achievements to be shared with a wide circle of researchers. Sections of the conference are of interest for the broad range of experts involved in developing innovative solutions and organizing events that increase the efficiency of economic and innovative activities.

Data Science and Big Data Analytics
Data Science and Big Data Analytics

Author: EMC Education Services

Publisher: John Wiley & Sons

Published: 2014-12-19

Total Pages: 432

ISBN-13: 1118876229

DOWNLOAD EBOOK

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

GPU Programming in MATLAB
GPU Programming in MATLAB

Author: Nikolaos Ploskas

Publisher: Morgan Kaufmann

Published: 2016-08-25

Total Pages: 320

ISBN-13: 0128051337

DOWNLOAD EBOOK

GPU programming in MATLAB is intended for scientists, engineers, or students who develop or maintain applications in MATLAB and would like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the Parallel Computing Toolbox and other MATLAB toolboxes for GPU computing, which allow applications to be ported straightforwardly onto GPUs without extensive knowledge of GPU programming. The next part covers built-in, GPU-enabled features of MATLAB, including options to leverage GPUs across multicore or different computer systems. Finally, advanced material includes CUDA code in MATLAB and optimizing existing GPU applications. Throughout the book, examples and source codes illustrate every concept so that readers can immediately apply them to their own development. - Provides in-depth, comprehensive coverage of GPUs with MATLAB, including the parallel computing toolbox and built-in features for other MATLAB toolboxes - Explains how to accelerate computationally heavy applications in MATLAB without the need to re-write them in another language - Presents case studies illustrating key concepts across multiple fields - Includes source code, sample datasets, and lecture slides

Foundations of Data Science
Foundations of Data Science

Author: Avrim Blum

Publisher: Cambridge University Press

Published: 2020-01-23

Total Pages: 433

ISBN-13: 1108617360

DOWNLOAD EBOOK

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.