Name: Deep Learning Through Sparse and Low-Rank Modeling (Computer Vision and Pattern Recognition) (en Inglés)
Price: 367155.00 COP
Availability: InStock
Author: Zhangyang Wang; Yun Fu; Thomas S. Huang
ISBN: 9780128136591

portada Deep Learning Through Sparse and Low-Rank Modeling (Computer Vision and Pattern Recognition) (en Inglés)

Deep Learning Through Sparse and Low-Rank Modeling (Computer Vision and Pattern Recognition) (en Inglés)

Formato

Libro Físico

Editorial

Academic Press

Autor

Zhangyang Wang; Yun Fu; Thomas S. Huang

Año

2019

Idioma

Inglés

N° páginas

296

Encuadernación

Tapa Blanda

ISBN13

9780128136591

N° edición

Categorías

Procesamiento de imágenes

Deep Learning Through Sparse and Low-Rank Modeling (Computer Vision and Pattern Recognition) (en Inglés)

Zhangyang Wang; Yun Fu; Thomas S. Huang (Autor) · Academic Press · Tapa Blanda

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Reseña del libro "Deep Learning Through Sparse and Low-Rank Modeling (Computer Vision and Pattern Recognition) (en Inglés)"

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