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Advancement Deep Learning Applications (14 results)
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Advancement of Deep Learning and Its Applications in Object Detection and Recognition
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Advancement of Deep Learning and Its Applications in Object Detection and Recognition
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Advancement of Deep Learning and its Applications in Object Detection and Recognition
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Hardcover. Condition: Brand New. 200 pages. 9.19x6.13x0.79 inches. In Stock.
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Hardcover. Condition: Brand New. 200 pages. 9.19x6.13x0.79 inches. In Stock. This item is printed on demand.
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Advancement of Deep Learning and its Applications in Object Detection and Recognition
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
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Add to basketHRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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HRD. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dr. Roohie N Mir is a professor in the Department of Computer Science & Engineering at NIT Srinagar, INDIA. She received her B.Eng. (Hons) in Electrical Engineering from University of Kashmir (India) in 1985, her M.Eng. in Computer S.
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Object detection is a basic visual identification problem in computer vision that has been explored extensively over the years. Visual object detection seeks to discover objects of specific target classes in a given image with pinpoint accuracy and apply a class label to each object instance. Object recognition strategies based on deep learning have been intensively investigated in recent years as a result of the remarkable success of deep learning-based image categorization.In this book, we go through in detail detector architectures, feature learning, proposal generation, sampling strategies, and other issues that affect detection performance.The book describes every newly proposed novel solution but skips through the fundamentals so that readers can see the field's cutting edge more rapidly. Moreover, unlike prior object detection publications, this project analyses deep learning-based object identification methods systematically and exhaustively, and also gives the most recent detection solutions and a collection of noteworthy research trends.The book focuses primarily on step-by-step discussion, an extensive literature review, detailed analysis and discussion, and rigorous experimentation results. Furthermore, a practical approach is displayed and encouraged.
