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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.
Numéro d'article:
71291687
Tensor Computation for Data Analysis
86% des répondants recommanderaient ceci à un ami
Numéro d'article:
71291687
CAD 199
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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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Ce qui se démarque
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.
Détails du produit
- 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) |
À qui est-ce destiné ?
-
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.
-
Beginners
Not suitable for beginners lacking foundational knowledge in data analysis or tensor mathematics.
DESCRIPTION DU PRODUIT
Tensor Computation for Data Analysis
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Product Buying Guide
Tensor Computation for Data Analysis 1st ed. 2022 Edition is a comprehensive guide that provides non-specialists with an overall understanding of tensor computation and its applications in data analysis. It is designed to benefit researchers, engineers, and students seeking theoretical, computational, technical, and experimental details in this field.
Product Specifications
- Title: Tensor Computation for Data Analysis 1st ed. 2022 Edition
- Release Year: 2022
- Edition: 1st Edition
- Format: Paperback
- Number of Pages: Varies
- Language: English
Key Features
- Provides an overall understanding of tensor computation and its applications in data analysis
- Presents theoretical, computational, technical, and experimental details
- Covers tensor decompositions, tensor-based data analysis techniques, and practical examples in various domains
- Includes application areas such as machine learning, signal processing, data mining, computer vision, and biomedical engineering
- Suitable for researchers, engineers, and students
Usage Scenarios
- Machine learning professionals can utilize the book to gain insights into tensor-based techniques and apply them in their work
- Researchers in signal processing and computer vision can learn about tensor computation for image reconstruction, enhancement, and human pose estimation
- Data analysts in various domains can enhance their data analysis techniques with tensor computation
- Students can use the book as a learning resource for understanding tensor computation and its applications
Usage Scenarios
- Introduction to Tensor Decompositions and their Applications by Tamara Kolda et al.
- Tensor Methods in Data Science by Maya R. Gupta
- Tensor Decompositions and Applications by Baltasar Beferull-Lozano et al.
Some User Review
- I found this book to be an excellent resource for understanding tensor computation techniques. The explanations are clear, and the practical examples provided make it easy to implement the concepts in real-world scenarios.
- As a researcher in the field of computer vision, this book has been immensely helpful in expanding my knowledge of tensor-based image reconstruction and human pose estimation. I highly recommend it to anyone working in similar domains.
- I am a student studying data analysis, and this book has been a great companion in helping me grasp the fundamentals of tensor computation. The comprehensive coverage and practical examples have made the learning process much easier.
Competitors
- The price of Tensor Computation for Data Analysis 1st ed. 2022 Edition is competitive compared to similar books in the market, providing an excellent value for the comprehensive content it offers.
- Considering the wealth of theoretical, computational, and practical information provided in this book, the price is justified for professionals and students looking to expand their knowledge in tensor computation for data analysis.
Buying Considerations
- Consider your level of expertise and background knowledge in tensor computation before purchasing this book. It is designed to benefit non-specialists seeking an overall understanding of the subject.
- If you are specifically interested in applications of tensor computation in machine learning, signal processing, computer vision, or data mining, this book provides practical examples and techniques in these areas.
- Take into account the availability of alternative resources such as competing books, online tutorials, or academic papers in the field of tensor computation before making a purchase decision.
Conclusion
Tensor Computation for Data Analysis 1st ed. 2022 Edition is a valuable resource for gaining insights into tensor computation and its applications in various domains. Whether you are a researcher, engineer, or student, this comprehensive guide will enhance your understanding of tensor-based data analysis techniques and provide practical examples for implementation.
Voir moinsTensor Computation for Data Analysis 1st ed. 2022 Edition is a comprehensive guide that provides non-specialists with an overall understanding of tensor computation and its applications in data analysis. It is designed to benefit researchers, engineers, and students seeking theoretical, computational, technical, and experimental details in this field. Continue Reading
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Avantages
- Comprehensive coverage of tensors
- User-friendly explanations
- Great for data analysis techniques
- Includes practical examples
- Well-structured and organized
Les inconvénients
- Some topics may require prior knowledge.
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Caractéristiques et avantages
- 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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Profitez d'une expérience d'achat sereine avec des produits 100 % originaux, une sécurité de paiement conforme PCI DSS, une protection des données certifiée ISO 27001, la livraison transfrontalière la plus rapide, les retours gratuits* et un emballage sécurisé pour chaque commande.