Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Deep Learning Through Sparse and Low-Rank Modeling

Deep Learning Through Sparse and Low-Rank Modeling

Wang, Zhangyang
Fu, Yun
Huang, Thomas S.

Paperback

Series: Computer Vision and Pattern Recognition

Technology & EngineeringGeneral Computers

ISBN10: 0128136596
ISBN13: 9780128136591
Publisher: Academic Press
Published: Apr 12 2019
Pages: 296
Weight: 1.13
Height: 0.62 Width: 7.50 Depth: 9.25
Language: English

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.

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