Norman Matloff – författare

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17 produkter

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17 produkter

Art of R Programming
AvNorman Matloff
Häftad, Engelska, 2011
470 kr
Skickas inom 3-6 vardagar
R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly. The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro. Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: Create artful graphs to visualize complex data sets and functions; Write more efficient code using parallel R and vectorization; Interface R with C/C++ and Python for increased speed or functionality; Find new R packages for text analysis, image manipulation, and more; Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.
Probability and Statistics for Data Science
Math + R + Data
AvNorman Matloff
Inbunden, Engelska, 2019
2 463 kr
Skickas inom 10-15 vardagar
Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously:* Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.* Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture."* Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner.Prerequisites are calculus, some matrix algebra, and some experience in programming.Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
Parallel Computing for Data Science
With Examples in R, C++ and CUDA
AvNorman Matloff
Häftad, Engelska, 2020
765 kr
Skickas inom 10-15 vardagar
Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools, and applications in data science. It includes examples not only from the classic "n observations, p variables" matrix format but also from time series, network graph models, and numerous other structures common in data science. The examples illustrate the range of issues encountered in parallel programming.With the main focus on computation, the book shows how to compute on three types of platforms: multicore systems, clusters, and graphics processing units (GPUs). It also discusses software packages that span more than one type of hardware and can be used from more than one type of programming language. Readers will find that the foundation established in this book will generalize well to other languages, such as Python and Julia.
Probability and Statistics for Data Science
Math + R + Data
AvNorman Matloff
E-bok
Engelska, 2019
1 127 kr
Läs direkt efter köp
Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously:
* Real datasets are used extensively.
* All data analysis is supported by R coding.
* Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.
* Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture."
* Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner.
Prerequisites are calculus, some matrix algebra, and some experience in programming.
Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university''s Distinguished Teaching Award.
Probability and Statistics for Data Science
Math + R + Data
AvNorman Matloff
E-bok
PDF, Engelska, 2019
1 157 kr
Läs direkt efter köp
Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously:
* Real datasets are used extensively.
* All data analysis is supported by R coding.
* Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.
* Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture."
* Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner.
Prerequisites are calculus, some matrix algebra, and some experience in programming.
Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university''s Distinguished Teaching Award.
Parallel Computing for Data Science
With Examples in R, C++ and CUDA
AvNorman Matloff
E-bok
Engelska, 2015
908 kr
Läs direkt efter köp
This is one of the first parallel computing books to focus exclusively on parallel data structures, algorithms, software tools, and applications in data science. The book prepares readers to write effective parallel code in various languages and learn more about different R packages and other tools. It covers the classic n observations, p variables matrix format and common data structures. Many examples illustrate the range of issues encountered in parallel programming.
Statistical Regression and Classification
From Linear Models to Machine Learning
AvNorman Matloff
Inbunden, Engelska, 2017
2 645 kr
Skickas inom 10-15 vardagar
This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned into a main body section and an extras section. The main body uses math stat very sparingly and always in the context of something concrete, which means that readers can skip the math stat content entirely if they wish. The extras section is for those who feel comfortable with analysis using math stat.
Probability and Statistics for Data Science
Math + R + Data
AvNorman Matloff
Häftad, Engelska, 2019
947 kr
Skickas inom 10-15 vardagar
Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously:* Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.* Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture."* Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner.Prerequisites are calculus, some matrix algebra, and some experience in programming.Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
Statistical Regression and Classification
From Linear Models to Machine Learning
AvNorman Matloff
E-bok
Engelska, 2017
1 358 kr
Läs direkt efter köp
This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned into a main body section and an extras section. The main body uses math stat very sparingly and always in the context of something concrete, which means that readers can skip the math stat content entirely if they wish. The extras section is for those who feel comfortable with analysis using math stat.
Parallel Computing for Data Science
With Examples in R, C++ and CUDA
AvNorman Matloff
Inbunden, Engelska, 2015
1 221 kr
Skickas inom 10-15 vardagar
Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools, and applications in data science. It includes examples not only from the classic "n observations, p variables" matrix format but also from time series, network graph models, and numerous other structures common in data science. The examples illustrate the range of issues encountered in parallel programming.With the main focus on computation, the book shows how to compute on three types of platforms: multicore systems, clusters, and graphics processing units (GPUs). It also discusses software packages that span more than one type of hardware and can be used from more than one type of programming language. Readers will find that the foundation established in this book will generalize well to other languages, such as Python and Julia.
Parallel Computing for Data Science
With Examples in R, C++ and CUDA
AvNorman Matloff
E-bok
PDF, Engelska, 2015
933 kr
Läs direkt efter köp
This is one of the first parallel computing books to focus exclusively on parallel data structures, algorithms, software tools, and applications in data science. The book prepares readers to write effective parallel code in various languages and learn more about different R packages and other tools. It covers the classic n observations, p variables matrix format and common data structures. Many examples illustrate the range of issues encountered in parallel programming.
Statistical Regression and Classification
From Linear Models to Machine Learning
AvNorman Matloff
Häftad, Engelska, 2017
1 144 kr
Skickas inom 10-15 vardagar
This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned into a main body section and an extras section. The main body uses math stat very sparingly and always in the context of something concrete, which means that readers can skip the math stat content entirely if they wish. The extras section is for those who feel comfortable with analysis using math stat.
Statistical Regression and Classification
From Linear Models to Machine Learning
AvNorman Matloff
E-bok
PDF, Engelska, 2017
1 358 kr
Läs direkt efter köp
This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned into a main body section and an extras section. The main body uses math stat very sparingly and always in the context of something concrete, which means that readers can skip the math stat content entirely if they wish. The extras section is for those who feel comfortable with analysis using math stat.
Art of Debugging with GDB, DDD, and Eclipse
AvPeter Jay Salzman,Norman Matloff
E-bok
Engelska, 2008
366 kr
Läs direkt efter köp
Debugging is crucial to successful software development, but even many experienced programmers find it challenging. Sophisticated debugging tools are available, yet it may be difficult to determine which features are useful in which situations. The Art of Debugging is your guide to making the debugging process more efficient and effective.The Art of Debugging illustrates the use three of the most popular debugging tools on Linux/Unix platforms: GDB, DDD, and Eclipse. The text-command based GDB (the GNU Project Debugger) is included with most distributions. DDD is a popular GUI front end for GDB, while Eclipse provides a complete integrated development environment.In addition to offering specific advice for debugging with each tool, authors Norm Matloff and Pete Salzman cover general strategies for improving the process of finding and fixing coding errors, including how to:–Inspect variables and data structures–Understand segmentation faults and core dumps–Know why your program crashes or throws exceptions–Use features like catchpoints, convenience variables, and artificial arrays–Avoid common debugging pitfallsReal world examples of coding errors help to clarify the authors’ guiding principles, and coverage of complex topics like thread, client-server, GUI, and parallel programming debugging will make you even more proficient. You''ll also learn how to prevent errors in the first place with text editors, compilers, error reporting, and static code checkers.Whether you dread the thought of debugging your programs or simply want to improve your current debugging efforts, you''ll find a valuable ally in The Art of Debugging.
Art of R Programming
A Tour of Statistical Software Design
AvNorman Matloff
E-bok
Engelska, 2011
454 kr
Läs direkt efter köp
R is the world''s most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly.The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.Along the way, you''ll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You''ll also learn to:–Create artful graphs to visualize complex data sets and functions–Write more efficient code using parallel R and vectorization–Interface R with C/C++ and Python for increased speed or functionality–Find new R packages for text analysis, image manipulation, and more–Squash annoying bugs with advanced debugging techniquesWhether you''re designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.
Art of Machine Learning
A Hands-On Guide to Machine Learning with R
AvNorman Matloff
Häftad, Engelska, 2024
470 kr
Skickas inom 3-6 vardagar
Machine learning without advanced math! This book presents a serious, practical look at machine learning, preparing you for valuable insights on your own data. The Art of Machine Learning is packed with real dataset examples and sophisticated advice on how to make full use of powerful machine learning methods. Readers will need only an intuitive grasp of charts, graphs, and the slope of a line, as well as familiarity with the R programming language. You'll become skilled in a range of machine learning methods, starting with the simple k-Nearest Neighbours method (k-NN), then on to random forests, gradient boosting, linear/logistic models, support vector machines, the LASSO, and neural networks. Final chapters introduce text and image classification, as well as time series. You'll learn not only how to use machine learning methods, but also why these methods work, providing the strong foundational background you'll need in practice. Additional features: How to avoid common problems, such as dealing with 'dirty' data and factor variables with large numbers of levels; A look at typical misconceptions, such as dealing with unbalanced data; Exploration of the famous Bias-Variance Tradeoff, central to machine learning, and how it plays out in practice for each machine learning method; Dozens of illustrative examples involving real datasets of varying size and field of application; Standard R packages are used throughout, with a simple wrapper interface to provide convenient access. After finishing this book, you will be well equipped to start applying machine learning techniques to your own datasets.
Art of Machine Learning
AvNorman Matloff
E-bok
Engelska, 2024
461 kr
Läs direkt efter köp

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Norman Matloff – författare

Art of R Programming

Probability and Statistics for Data Science

Parallel Computing for Data Science

Probability and Statistics for Data Science

Probability and Statistics for Data Science

Parallel Computing for Data Science

Statistical Regression and Classification

Probability and Statistics for Data Science

Statistical Regression and Classification

Parallel Computing for Data Science

Parallel Computing for Data Science

Statistical Regression and Classification

Statistical Regression and Classification

Art of Debugging with GDB, DDD, and Eclipse

Art of R Programming

Art of Machine Learning

Art of Machine Learning

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