Author: Darrell A. Turkington

Publisher: Cambridge University Press

ISBN: 1107032008

Pages: 267

Year: 2013-02-11

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"This book studies the mathematics behind matrix calculus, and the final chapter looks at applications of matrix calculus in statistics and econometrics"--

Author: Adilifu Nama

Publisher: University of Texas Press

ISBN: 029277236X

Pages: 184

Year: 2015-04-15

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Known for their violence and prolific profanity, including free use of the n-word, the films of Quentin Tarantino, like the director himself, chronically blurt out in polite company what is extremely problematic even when deliberated in private. Consequently, there is an uncomfortable and often awkward frankness associated with virtually all of Tarantino's films, particularly when it comes to race and blackness. Yet beyond the debate over whether Tarantino is or is not racist is the fact that his films effectively articulate racial anxieties circulating in American society as they engage longstanding racial discourses and hint at emerging trends. This radical racial politics—always present in Tarantino's films but kept very much on the quiet—is the subject of Race on the QT. Adilifu Nama concisely deconstructs and reassembles the racial dynamics woven into Reservoir Dogs, True Romance, Pulp Fiction, Jackie Brown, Kill Bill: Vol. 1, Kill Bill: Vol. 2, Death Proof, Inglourious Basterds, and Django Unchained, as they relate to historical and current racial issues in America. Nama's eclectic fusion of cultural criticism and film analysis looks beyond the director's personal racial attitudes and focuses on what Tarantino's filmic body of work has said and is saying about race in America symbolically, metaphorically, literally, impolitely, cynically, sarcastically, crudely, controversially, and brilliantly.

Author:

Publisher: CRC Press

ISBN: 1420082515

Pages: 446

Year: 2008-11-03

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This textbook presents a variety of applied mathematics topics in science and engineering with an emphasis on problem solving techniques using MATLAB®. The authors provide a general overview of the MATLAB language and its graphics abilities before delving into problem solving, making the book useful for readers without prior MATLAB experience. They explain how to generate code suitable for various applications so that readers can apply the techniques to problems not covered in the book. Examples, figures, and MATLAB scripts enable readers with basic mathematics knowledge to solve various applied math problems in their fields while avoiding unnecessary technical details.

Author: Florian Hahne, Wolfgang Huber, Robert Gentleman, Seth Falcon

Publisher: Springer Science & Business Media

ISBN: 0387772405

Pages: 284

Year: 2010-06-09

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Bioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include: (1) import and preprocessing of data from various sources; (2) statistical modeling of differential gene expression; (3) biological metadata; (4) application of graphs and graph rendering; (5) machine learning for clustering and classification problems; (6) gene set enrichment analysis. Each chapter of this book describes an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced considerations of key topics. The book is a dynamic document. All the code shown can be executed on a local computer, and readers are able to reproduce every computation, figure, and table.

Author: Peter van Overschee, B.L. de Moor

Publisher: Springer Science & Business Media

ISBN: 1461304652

Pages: 272

Year: 2012-12-06

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Subspace Identification for Linear Systems focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finite- dimensional dynamical systems. These algorithms allow for a fast, straightforward and accurate determination of linear multivariable models from measured input-output data. The theory of subspace identification algorithms is presented in detail. Several chapters are devoted to deterministic, stochastic and combined deterministic-stochastic subspace identification algorithms. For each case, the geometric properties are stated in a main 'subspace' Theorem. Relations to existing algorithms and literature are explored, as are the interconnections between different subspace algorithms. The subspace identification theory is linked to the theory of frequency weighted model reduction, which leads to new interpretations and insights. The implementation of subspace identification algorithms is discussed in terms of the robust and computationally efficient RQ and singular value decompositions, which are well-established algorithms from numerical linear algebra. The algorithms are implemented in combination with a whole set of classical identification algorithms, processing and validation tools in Xmath's ISID, a commercially available graphical user interface toolbox. The basic subspace algorithms in the book are also implemented in a set of Matlab files accompanying the book. An application of ISID to an industrial glass tube manufacturing process is presented in detail, illustrating the power and user-friendliness of the subspace identification algorithms and of their implementation in ISID. The identified model allows for an optimal control of the process, leading to a significant enhancement of the production quality. The applicability of subspace identification algorithms in industry is further illustrated with the application of the Matlab files to ten practical problems. Since all necessary data and Matlab files are included, the reader can easily step through these applications, and thus get more insight in the algorithms. Subspace Identification for Linear Systems is an important reference for all researchers in system theory, control theory, signal processing, automization, mechatronics, chemical, electrical, mechanical and aeronautical engineering.

Author: Patrick Cozzi, Christophe Riccio

Publisher: CRC Press

ISBN: 1439893764

Pages: 712

Year: 2012-07-23

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Get Real-World Insight from Experienced Professionals in the OpenGL Community With OpenGL, OpenGL ES, and WebGL, real-time rendering is becoming available everywhere, from AAA games to mobile phones to web pages. Assembling contributions from experienced developers, vendors, researchers, and educators, OpenGL Insights presents real-world techniques for intermediate and advanced OpenGL, OpenGL ES, and WebGL developers. Go Beyond the Basics The book thoroughly covers a range of topics, including OpenGL 4.2 and recent extensions. It explains how to optimize for mobile devices, explores the design of WebGL libraries, and discusses OpenGL in the classroom. The contributors also examine asynchronous buffer and texture transfers, performance state tracking, and programmable vertex pulling. Sharpen Your Skills Focusing on current and emerging techniques for the OpenGL family of APIs, this book demonstrates the breadth and depth of OpenGL. Readers will gain practical skills to solve problems related to performance, rendering, profiling, framework design, and more.

Author: Giuseppe Ciaburro, Balaji Venkateswaran

Publisher: Packt Publishing Ltd

ISBN: 1788399412

Pages: 270

Year: 2017-09-27

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Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.

Author: National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, Committee on Applied and Theoretical Statistics, Committee on the Analysis of Massive Data

Publisher: National Academies Press

ISBN: 0309287812

Pages: 190

Year: 2013-09-03

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Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale--terabytes and petabytes--is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge--from computer science, statistics, machine learning, and application disciplines--that must be brought to bear to make useful inferences from massive data.

Author: Barbara Chapman, Federico Massaioli, Matthias S. Müller, Marco Rorro

Publisher: Springer

ISBN: 3642309615

Pages: 280

Year: 2012-05-23

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This book constitutes the refereed proceedings of the 8th International Workshop on OpenMP, held in in Rome, Italy, in June 2012. The 18 technical full papers presented together with 7 posters were carefully reviewed and selected from 30 submissions. The papers are organized in topical sections on proposed extensions to OpenMP, runtime environments, optimization and accelerators, task parallelism, validations and benchmarks

Author: Darrell A. Turkington

Publisher: Cambridge University Press

ISBN: 0521022452

Pages: 220

Year: 2005-11-10

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This 2002 book presents the reader with mathematical tools taken from matrix calculus and zero-one matrices and demonstrates how these tools greatly facilitate the application of classical statistical procedures to econometric models. The matrix calculus results are derived from a few basic rules that are generalizations of the rules of ordinary calculus. These results are summarized in a useful table. Well-known zero-one matrices, together with some newer ones, are defined, their mathematical roles explained, and their useful properties presented. The basic building blocks of classical statistics, namely the score vector, the information matrix, and the Cramer-Rao lower bound, are obtained for a sequence of linear econometric models of increasing statistical complexity. From these are obtained interactive interpretations of maximum likelihood estimators, linking them with efficient econometric estimators. Classical test statistics are also derived and compared for hypotheses of interest.

Author: William N. Venables, David M. Smith

Publisher: Network Theory.

ISBN: 0954612086

Pages: 144

Year: 2009

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This tutorial manual provides an introduction to R, a software package for statistical computing and graphics. This revised and updated edition is based on the April 2009 release of R (version 2.9.0).

Author: Roger A. Horn, Charles R. Johnson

Publisher: Cambridge University Press

ISBN: 0521386322

Pages: 561

Year: 1990-02-23

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In this book the authors present classical and recent results for matrix analysis that have proved to be important to applied mathematics. Facts about matrices, beyond those found in an elementary linear algebra course, are needed to understand virtually any area of mathematics, and the necessary material has only occurred sporadically in the literature and university curricula. As the interest in applied mathematics has grown, the need for a text and a reference work offering a broad selection of topics has become apparent, and this book aims to meet that need. This book will be welcomed as an undergraduate or graduate textbook for students studying matrix analysis. The authors assume a background in elementary linear algebra and knowledge of rudimentary analytical concepts. They begin with a review and discussion of eigenvalues and eigenvectors. The following chapters each treat a major topic in depth. This volume should be useful not only as a text, but also as a self-contained reference work to a variety of audiences in other scientific fields.

Author: Michiel Hazewinkel

Publisher: Springer Science & Business Media

ISBN: 9401512884

Pages: 588

Year: 2012-12-06

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This is the first Supplementary volume to Kluwer's highly acclaimed Encyclopaedia of Mathematics. This additional volume contains nearly 600 new entries written by experts and covers developments and topics not included in the already published 10-volume set. These entries have been arranged alphabetically throughout. A detailed index is included in the book. This Supplementary volume enhances the existing 10-volume set. Together, these eleven volumes represent the most authoritative, comprehensive up-to-date Encyclopaedia of Mathematics available.

Author: Norman Matloff

Publisher: No Starch Press

ISBN: 1593273843

Pages: 400

Year: 2011

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A guide to software development using the R programming language covers such topics as closures, recursion, anonymous functions, and debugging techniques.

Author: Seymour Lipschutz, Marc Lipson

Publisher: Erlangga

ISBN: 9797815714

Pages: 424

Year: 2001

View: 521

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• This third edition of the successful outline in linear algebra—which sold more than 400,000 copies in its past two editions—has been thoroughly updated to increase its applicability to the fields in which linear algebra is now essential: computer science, engineering, mathematics, physics, and quantitative analysis• Revised coverage includes new problems relevant to computer science and a revised chapter on linear equations• More than 100,000 students enroll in beginning and advanced Linear Algebra courses each year. This outline is appropriate for both first- and second-level linear algebra courses