Kumar Nirman (42 results)
Random Number Generators for Computer Simulation and Cyber Security: Design, Search, Theory, and Application (Synthesis Lectures on Mathematics & Statistics)
Deng, Lih-Yuan; Kumar, Nirman; Lu, Henry Horng-Shing; Yang, Ching-Chi
- Hardcover
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Dimensionality Reduction in Data Science
Garzon, Max (EDT); Yang, Ching-chi (EDT); Venugopal, Deepak (EDT); Kumar, Nirman (EDT); Jana, Kalidas (EDT)
- Hardcover
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Random Number Generators for Computer Simulation and Cyber Security: Design, Search, Theory, and Application (Synthesis Lectures on Mathematics & Statistics)
Deng, Lih-Yuan; Kumar, Nirman; Lu, Henry Horng-Shing; Yang, Ching-Chi
- Hardcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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- Hardcover
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Dimensionality Reduction in Data Science
Garzon, Max (EDT); Yang, Ching-chi (EDT); Venugopal, Deepak (EDT); Kumar, Nirman (EDT); Jana, Kalidas (EDT)
- Hardcover
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Language: English
Published by Springer International Publishing AG, Cham 2022
- Hardcover
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Hardcover. Condition: new. Hardcover. This book provides a practical and fairly comprehensive review of Data Science through the lens of dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and solutions from statistics, computer science and m…athematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated.The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains.This book focuses on data science and problem definition, data cleansing, feature selection and extraction,statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting.This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages to other solutions. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
More imagesDimensionality Reduction in Data Science
Deepak Venugopal, Nirman Kumar, Ching-chi Yang, Max Garzon, Kalidas Jana
Language: English
Published by Springer International Publishing AG, CH 2022
- Hardcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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Hardback. Condition: New. 2022 ed. This book provides a practical and fairly comprehensive review of Data Science through the lens of dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and solutions from statistics, computer science and math…ematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated.The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains.This book focuses on data science and problem definition, data cleansing, feature selection and extraction,statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting.This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages to other solutions.
Dimensionality Reduction in Data Science
Garzon, Max (EDT); Yang, Ching-chi (EDT); Venugopal, Deepak (EDT); Kumar, Nirman (EDT); Jana, Kalidas (EDT)
- Hardcover
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Dimensionality Reduction in Data Science
Max Garzon, Ching-Chi Yang, Deepak Venugopal, Nirman Kumar, Kalidas Jana, Lih-Yuan Deng
- Hardcover
Seller: Chiron Media, Wallingford, United KingdomChiron Media
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Random Number Generators for Computer Simulation and Cyber Security: Design, Search, Theory, and Application (Synthesis Lectures on Mathematics & Statistics)
Deng, Lih-Yuan; Kumar, Nirman; Lu, Henry Horng-Shing; Yang, Ching-Chi
- Hardcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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- Hardcover
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Dimensionality Reduction in Data Science
Garzon, Max (EDT); Yang, Ching-chi (EDT); Venugopal, Deepak (EDT); Kumar, Nirman (EDT); Jana, Kalidas (EDT)
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
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Random Number Generators for Computer Simulation and Cyber Security: Design, Search, Theory, and Application
Deng, Lih-yuan/ Kumar, Nirman/ Lu, Henry Horng-Shing/ Yang, Ching-chi
- Hardcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 250 pages. 9.45x6.62x9.61 inches. In Stock.
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Dimensionality Reduction in Data Science
Garzon, Max (Editor) / Yang, Ching-Chi (Editor) / Venugopal, Deepak (Editor) / Kumar, Nirman (Editor) / Jana, Kalidas (Editor) / Deng, Lih-Yuan (Editor)
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 276 pages. 9.25x6.10x0.67 inches. In Stock.
- Hardcover
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Language: English
Published by Springer, Berlin|Springer International Publishing|Springer 2023
- Softcover
Seller: moluna, Greven, Germanymoluna
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- Hardcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 276 pages. 9.25x6.10x0.69 inches. In Stock.
- Softcover
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a practical and fairly comprehensive review of Data Science through the lensof dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and soluti…ons from statistics, computer science and mathematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated.The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains.This book focuses on data science and problem definition, data cleansing, feature selection and extraction,statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting.This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages toother solutions.
Language: English
Published by Springer, Berlin|Springer International Publishing|Springer 2022
- Hardcover
Seller: moluna, Greven, Germanymoluna
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- Softcover
Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: Neu. Dimensionality Reduction in Data Science | Max Garzon (u. a.) | Taschenbuch | xi | Englisch | 2023 | Springer | EAN 9783031053733 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
- Hardcover
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a practical and fairly comprehensive review of Data Science through the lensof dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and solutions fro…m statistics, computer science and mathematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated.The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains.This book focuses on data science and problem definition, data cleansing, feature selection and extraction,statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting.This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages toother solutions.
Language: English
Published by Springer International Publishing AG, Cham 2022
- Hardcover
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Hardcover. Condition: new. Hardcover. This book provides a practical and fairly comprehensive review of Data Science through the lens of dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and solutions from statistics, computer science and m…athematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated.The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains.This book focuses on data science and problem definition, data cleansing, feature selection and extraction,statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting.This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages to other solutions. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Dimensionality Reduction in Data Science
Deepak Venugopal, Nirman Kumar, Ching-chi Yang, Max Garzon, Kalidas Jana
Language: English
Published by Springer International Publishing AG, CH 2022
- Hardcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
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Hardback. Condition: New. 2022 ed. This book provides a practical and fairly comprehensive review of Data Science through the lens of dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and solutions from statistics, computer science and math…ematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated.The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains.This book focuses on data science and problem definition, data cleansing, feature selection and extraction,statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting.This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages to other solutions.
- Hardcover
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Condition: Hervorragend. Zustand: Hervorragend | Seiten: 280 | Sprache: Englisch | Produktart: Bücher | This book provides a practical and fairly comprehensive review of Data Science through the lens of dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the…-art results and solutions from statistics, computer science and mathematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated.The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains.This book focuses on data science and problem definition, data cleansing, feature selection and extraction,statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting.This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages to other solutions.
- Hardcover
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Hardcover. Condition: gut. 2022. Dimensionality Reduction in Data Science In deutscher Sprache. pages.
Random Number Generators for Computer Simulation and Cyber Security: Design, Search, Theory, and Application
Deng, Lih-yuan/ Kumar, Nirman/ Lu, Henry Horng-Shing/ Yang, Ching-chi
- Hardcover
- Print on Demand
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Hardcover. Condition: Brand New. 250 pages. 9.45x6.62x9.61 inches. In Stock. This item is printed on demand.
- Softcover
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Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
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