عرض عادي

Deep learning through sparse and low-rank modeling / edited by Zhangyang Wang, Yun Fu, Thomas S. Huang.

المساهم (المساهمين):نوع المادة : نصنصاللغة: الإنجليزية السلاسل:Computer vision and pattern recognition seriesالناشر:London ; San Diego : Academic Press, an imprint of Elsevier, 2019تاريخ حقوق النشر: �2019وصف:xvii, 277 pages ; 24 cmنوع المحتوى:
  • text
نوع الوسائط:
  • unmediated
نوع الناقل:
  • volume
تدمك:
  • 9780128136591
  • 0128136596
الموضوع:تصنيف مكتبة الكونجرس:
  • Q325.5 .D444 2019
ملخص: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.
المقتنيات
نوع المادة المكتبة الحالية رقم الطلب رقم النسخة حالة تاريخ الإستحقاق الباركود
كتاب كتاب UAE Federation Library | مكتبة اتحاد الإمارات General Collection | المجموعات العامة Q325.5 .D444 2019 (إستعراض الرف(يفتح أدناه)) C.1 Library Use Only | داخل المكتبة فقط 30020000208097

Includes bibliographical references and index.

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.

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