What is Tensor Computation for Data Analysis about?

Tensor Computation for Data Analysis provides an in-depth exploration of how tensors and tensor algebra can be utilized for data analysis. This book covers the mathematical foundations of tensor computations and their applications in machine learning, artificial intelligence, and data science. It emphasizes practical examples that demonstrate the versatility of tensor operations, making it a valuable resource for both beginners and experienced analysts seeking to enhance their data processing skills.

Who is the target audience for this book?

The target audience for Tensor Computation for Data Analysis includes data scientists, researchers, and students in computer science or mathematics. It also appeals to professionals in artificial intelligence and machine learning who are looking to deepen their understanding of tensor algebra. By offering detailed insights and practical applications, the book serves as a bridge between theoretical concepts and real-world implementations, making it essential for anyone involved in data analysis.

What types of applications can I find in this book?

This book showcases a variety of applications of tensor computation across fields such as image processing, natural language processing, and signal processing. You will find practical examples that illustrate how tensors can optimize computational solutions for large datasets, enhance neural network performance, and improve model accuracy. These use cases not only clarify concepts but also inspire innovative approaches to data analysis challenges.

Does the book include practical examples or exercises?

Yes, Tensor Computation for Data Analysis includes numerous practical examples and exercises designed to reinforce the reader's understanding. By engaging with hands-on tasks and real-world scenarios, readers can apply the theoretical knowledge they gain in a practical context. This approach makes it easier to grasp complex concepts and encourages readers to experiment with tensor computations in their own projects.

What level of mathematical knowledge is required to understand this book?

A foundational understanding of linear algebra and calculus is recommended for readers of Tensor Computation for Data Analysis. While the book introduces tensor concepts in an accessible manner, familiarity with matrices, vectors, and basic mathematical principles will greatly enhance the learning experience. This prior knowledge allows readers to effectively engage with both the theoretical aspects and practical applications discussed throughout the text.

Can this book help with machine learning projects?

Absolutely! Tensor Computation for Data Analysis is a great resource for enhancing machine learning projects. The book highlights how tensors can be effectively employed in optimization and modeling tasks, particularly in deep learning. By understanding the principles of tensor algebra, readers can implement more robust algorithms, handle large datasets more efficiently, and achieve better performance in their machine learning applications.

Is this book suitable for self-study?

Yes, the structure and content of Tensor Computation for Data Analysis make it suitable for self-study. The book is designed with clarity in mind, allowing readers to progress at their own pace. With its mix of theoretical insights, practical examples, and exercises, self-learners can develop a solid understanding of tensor computations independently, making it an effective study tool for aspiring data analysts.

What programming languages or software does the book use?

The book utilizes popular programming languages and libraries such as Python and TensorFlow to illustrate tensor computations. These languages are widely used in the data science community, making the examples relevant and applicable in real-world scenarios. By following the coding implementations, readers can see how to leverage these tools for their own data analysis needs, enhancing their technical skills in programming and computation.

Where can I buy Tensor Computation for Data Analysis 1st ed. 2022 Edition in French Guiana?

You can buy Tensor Computation for Data Analysis 1st ed. 2022 Edition from Ubuy, a trusted online retailer that offers a variety of academic and technical books. Ubuy provides a seamless shopping experience, allowing you to find the book easily and enjoy prompt service tailored to your needs. Simply visit the Ubuy website and search for the title to make your purchase.

Are there any prerequisites before reading this book?

While Tensor Computation for Data Analysis is accessible, having basic knowledge of programming (especially in Python) and familiarity with data analysis concepts will significantly benefit the reader. Prerequisites such as an understanding of data structures and algorithms will allow readers to make the most out of the examples provided in the book. With these foundational skills, the transition to more complex tensor operations and applications will be smoother.

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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

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Numéro d'article: 71291687

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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.

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Tensor Computation for Data Analysis

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question: Tensor Computation for Data Analysis est-il disponible pour effectuer des achats en ligne à French Guiana ?

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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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Tensor Computation for Data Analysis

€ 122

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Ce qui se démarque

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