eBook: Deep Learning through Sparse and Low-Rank Modeling (DRM EPUB)
 
電子書格式: DRM EPUB
作者: Zhangyang Wang, Yun Fu, Thomas S. Huang 
系列: Computer Vision and Pattern Recognition
分類: Computer networking & communications ,
Computer vision ,
Image processing  
書城編號: 20173863


售價: $1000.00

購買後立即進貨, 約需 1-4 天

 
 
製造商: Elsevier Science
出版日期: 2019/04/11
頁數: 296
ISBN: 9780128136607
 
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商品簡介
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.Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networksShows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning modelsProvides tactics on how to build and apply customized deep learning models for various applications
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