Tensor Computation for Data Analysis
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Tensor Computation for Data Analysis
This book is intended to provide non-specialists an overall understanding of tensor computation and its applications in data analysis and presents a systematic and up-to-date overview of tensor decompositions from the engineer's point of view, including tensor extensions of classical matrix-based data analysis techniques. It also includes practical examples in machine learning, signal processing, and other fields.
Tensor Computation for Data Analysis
Nº de artículo: 71291687

Tensor Computation for Data Analysis

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Nº de artículo: 71291687

BOB 942

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This book is intended to provide non-specialists an overall understanding of tensor computation and its applications in data analysis and presents a systematic and up-to-date overview of tensor decompositions from the engineer's point of view, including tensor extensions of classical matrix-based data analysis techniques. It also includes practical examples in machine learning, signal processing, and other fields.
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What Stands Out

Comprehensive Coverage
This edition provides an in-depth exploration of tensor computation, making complex data analysis techniques accessible to students and professionals alike.
Practical Applications
Focuses on real-world applications of tensor methods in data analysis, equipping readers with skills directly applicable in industries like machine learning and artificial intelligence.
User-Friendly Format
Structured with clear explanations and examples, this book simplifies tensor concepts, ensuring that both beginners and experienced practitioners can effectively enhance their data analysis capabilities.

Detalles de producto

Shop the 1st edition 2022 of Tensor Computation for Data Analysis at Ubuy Bolivia. Discover new ways to analyze data using tensor computation techniques.
  • Provides an understanding of tensor computation and its applications in data analysis for non-specialists, researchers, engineers, and students
  • Offers a systematic overview of tensor decompositions and comprehensive coverage of tensor computation-based data analysis techniques
  • Presents practical examples in machine learning, signal processing, data mining, computer vision, remote sensing, and biomedical engineering for easy understanding and implementation
  • The book also discusses potential applications in neuroscience, communication, psychometrics, chemometrics, biometrics, quantum physics, quantum chemistry, etc.
  • Covers basic notations, preliminary operations in tensor computations, main tensor decompositions and their properties, and tensor-based data analysis techniques
  • Includes typical applications with experimental results such as image reconstruction, image enhancement, data fusion, signal recovery, recommendation system, knowledge graph acquisition, traffic flow prediction, and more
Publisher Springer
Publication date September 2, 2022
Edition 2022nd
Language English
Print length 358 pages
ISBN-10 3030743888
ISBN-13 978-3030743888
Item Weight 1.24 pounds (560 grams)
Dimensions 6.1 x 0.82 x 9.25 inches (15.5 x 2.1 x 23.5 cm)

Who Should Buy?

Suitable For
  • Data Scientists

    Ideal for data scientists looking to enhance their analytical skills with advanced tensor computation techniques.

  • Graduate Students

    Beneficial for graduate students studying data analysis or machine learning, providing essential theoretical foundations and practical applications.

  • AI Researchers

    Great for AI researchers needing to implement tensor operations efficiently in their machine learning algorithms.

Not Suitable For
  • Beginners

    Not suitable for beginners lacking foundational knowledge in data analysis or tensor mathematics.

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Tensor Computation for Data Analysis

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ventajas

  • Comprehensive coverage of tensors
  • User-friendly explanations
  • Great for data analysis techniques
  • Includes practical examples
  • Well-structured and organized

Contras

  • Some topics may require prior knowledge.

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características y beneficios

  • Provides an overall understanding of tensor computation and its applications in data analysis.
  • Presents a systematic and up-to-date overview of tensor decompositions from an engineer's point of view.
  • Includes practical examples in machine learning, signal processing, and other fields.
  • Covers tensor dictionary learning, low rank tensor recovery, tensor completion, coupled tensor analysis, tensor regression, and others.
  • Additionally, this book includes a number of typical applications with experimental results.
  • The practical data analysis techniques presented can be applied in a wide range of applications such as neuroscience, quantum chemistry, and more.

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