Venugopal deepak (53 résultats)
Dimensionality Reduction in Data Science
Garzon, Max (EDT); Yang, Ching-chi (EDT); Venugopal, Deepak (EDT); Kumar, Nirman (EDT); Jana, Kalidas (EDT)
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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)
- Couverture rigide
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Hardcover. Etat : 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 mathe…matics 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.
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Hardback. Etat : 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 mathemat…ics 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)
- Couverture rigide
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Dimensionality Reduction in Data Science
Max Garzon, Ching-Chi Yang, Deepak Venugopal, Nirman Kumar, Kalidas Jana, Lih-Yuan Deng
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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)
- Couverture rigide
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-UniGreatBookPricesUK
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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)
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Taschenbuch. Etat : 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…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.
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Taschenbuch. Etat : 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.
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Hardcover. Etat : 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 mathe…matics 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.
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Buch. Etat : 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 from st…atistics, 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.
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Dimensionality Reduction in Data Science
Deepak Venugopal, Nirman Kumar, Ching-chi Yang, Max Garzon, Kalidas Jana
- Couverture rigide
Vendeur : Rarewaves.com UK, London, Royaume-UniRarewaves.com UK
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Hardback. Etat : 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 mathemat…ics 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.
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Taschenbuch. Etat : Neu. Big Data and Artificial Intelligence | 13th International Conference, BDA 2025, Bangalore, India, July 17-20, 2025, Proceedings | Rajeev Gupta (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xiv | Englisch | 2026 | Springer | EAN 9783032151339 | Verantwortliche Person für die EU: Springer Ver…lag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the proceedings of the 13th International Conference on Big Data and Artificial Intelligence, BDA 2025, held in Bangalore, India, during July 17 20, 2025.The 15 full papers and 13 short papers included in this book were carefully…reviewed and selected from 104 submissions. They were organized in topical sections as follows: Language Understanding and Interactive AI;Learning Paradigms and Optimisations; ML Frameworks and System-Level Intelligence; Deep Learning Architectures and Model Design; Deep Learning Architecture and Adaptations; Domain-Specific AI Models; and Fine-Tuning and Generative Modeling Techniques.
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Vendeur : Buchpark, Trebbin, AllemagneBuchpark
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Etat : 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.
