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Item #:
43487383
Computer Vision and Pattern Recognition Deep Learning Through Sparse and Low-Rank Modeling, (Paperback)
Item #:
43487383
USD 151
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Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models with recent deep network models, providing a rich variety of theoretical and analytic tools.
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What Stands Out
Deep Learning Focus
Provides an in-depth exploration of deep learning techniques specifically applied to computer vision, ensuring readers gain crucial insights for modern applications across various industries.
Sparse Modeling Techniques
Covers cutting-edge sparse and low-rank modeling methods, helping readers understand and effectively utilize these approaches to optimize performance in visual perception tasks.
Practical Applications
Includes real-world case studies, demonstrating the practical implementation of concepts, making it an invaluable resource for both students and professionals seeking hands-on experience in the field.
Product Details
- Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models--those that emphasize problem-specific Interpretability--with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | April, 2019 |
| Pages | 296 |
| Subgenre | Image Processing |
| Series title | Computer Vision and Pattern Recognition |
| Edition | 1 |
| Publisher | Elsevier Science |
| Original languages | English |
| Language | English |
| Edu focus | Technology |
| Educational level | College |
| Is collectible | N |
| Binding type | Perfect Binding |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 7.50 x 0.62 x 9.25 in (19.1 x 1.6 x 23.5 cm) |
| Assembled product weight | 1.13 lb (510 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
-
Graduate Students
Ideal for graduate students specializing in computer vision or artificial intelligence looking to deepen their knowledge in deep learning.
-
Data Scientists
Data scientists seeking advanced techniques in pattern recognition and sparse modeling to enhance their machine learning skills.
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Research Professionals
Research professionals aiming to explore cutting-edge methodologies in computer vision and implement them in various projects.
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Beginners
Not suitable for beginners due to its complex content and assumption of prior knowledge in deep learning concepts.
Product Description
About This Item
Introducing "Computer Vision and Pattern Recognition: Deep Learning Through Sparse and Low-Rank Modeling" - the ultimate guide for anyone interested in advancing their knowledge and skills in the fields of computer vision and pattern recognition. Authored by Zhangyang Wang, Yun Fu, and Thomas S Huang, this comprehensive book provides in-depth insights into the intricate concepts and techniques behind computer vision and pattern recognition. By leveraging the power of deep learning through sparse and low-rank modeling, you'll be empowered to tackle challenging problems and applications in these domains. This paperback edition is the perfect format for both beginners looking to dive into this fascinating field and experienced professionals seeking to expand their expertise.
The clear and concise writing style ensures that complex topics are presented in a way that is accessible and easy to understand. Published on April 12, 2019, this book is a valuable resource for those interested in e-commerce computer vision, pattern recognition in e-commerce, and deep learning for e-commerce. Whether you are a student, researcher, or industry professional, this book will equip you with the necessary knowledge and tools to excel in the rapidly evolving world of computer vision and pattern recognition. Don't miss out on this opportunity to enhance your understanding and proficiency in computer vision and pattern recognition. Order your copy of "Computer Vision and Pattern Recognition: Deep Learning Through Sparse and Low-Rank Modeling" today and embark on a transformative journey into the future of technology and innovation.
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Zhangyang Wang All Books Editorial Review
Computer Vision and Pattern Recognition Deep Learning Through Sparse and Low-Rank Modeling is a comprehensive guide that dives deep into the applications of deep learning techniques in image processing. This non-fiction paperback, published by Elsevier Science, consists of 296 pages filled with in-depth knowledge suitable for college-level education. The book emphasizes the significance of sparse and low-rank modeling in the realm of computer vision, making it an essential read for technology enthusiasts and professionals aiming to enhance their expertise in the field. Readers appreciate its clear explanations and structured content, providing a solid foundation in computational methods for image analysis.
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Pros
- Comprehensive exploration of deep learning techniques
- Suitable for college-level education
- Well-structured and easy to understand
- Focus on image processing applications
- Published by a reputable publisher
Cons
- Might be too technical for general readers
Product Price History
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USD 151
Order now and get it around Wednesday, October 21
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Features & Benefits
- Bridges classical sparse and low rank models with deep network models
- Provides a rich variety of theoretical and analytic tools
- Supports development of theory and models with applications in various fields
- Highly useful for researchers, graduate students, and practitioners
- Focuses on computer vision, machine learning, signal processing, optimization, and statistics
- Published in 2019 with 296 pages
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