Seller: WorldofBooks, Goring-By-Sea, WS, United Kingdom
£ 19.92
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Add to basketPaperback. Condition: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Seller: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germany
£ 10.39
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Add to basketxxiii, 282 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition.
Seller: Studibuch, Stuttgart, Germany
hardcover. Condition: Gut. 305 Seiten; 9783319575490.3 Gewicht in Gramm: 1.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
£ 46.58
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Add to basketCondition: As New. Unread book in perfect condition.
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
£ 55.64
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Published by Springer International Publishing, 2018
ISBN 10: 3319861905 ISBN 13: 9783319861906
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
£ 46.32
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis.Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website.This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.
Seller: Mispah books, Redhill, SURRE, United Kingdom
£ 57
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Add to basketPaperback. Condition: New. New. book.
Seller: Revaluation Books, Exeter, United Kingdom
£ 72.08
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Add to basketPaperback. Condition: Brand New. reprint edition. 282 pages. 9.50x6.25x0.75 inches. In Stock.
Published by Springer International Publishing, 2017
ISBN 10: 331957549X ISBN 13: 9783319575490
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
£ 69.49
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Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis.Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website.This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.
Seller: Mispah books, Redhill, SURRE, United Kingdom
£ 84
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Add to basketPaperback. Condition: New. New. book.
Published by Mechanical Industry Press, 2019
ISBN 10: 7111621964 ISBN 13: 9787111621966
Language: Chinese
Seller: liu xing, Nanjing, JS, China
paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2019-05-01 Publisher: Mechanical Industry Press Chapter 1 provides a quick review of the scientific principles of deep neural networks and the different frameworks for implementing such networks and the mathematical mechanisms behind them. Chapter 2 introduces the reader to convolutional neural networks and shows how to use deep learning to extract information from images. Chapter 3 builds on image classification problems from scratch.
Published by Packt Publishing Limited, 2018
ISBN 10: 1788392302 ISBN 13: 9781788392303
Language: English
Seller: PBShop.store US, Wood Dale, IL, U.S.A.
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Published by Packt Publishing Limited, 2018
ISBN 10: 1788392302 ISBN 13: 9781788392303
Language: English
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
£ 33.69
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Add to basketPAP. 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.
Published by Packt Publishing, Limited, 2018
ISBN 10: 1788392302 ISBN 13: 9781788392303
Language: English
Seller: Majestic Books, Hounslow, United Kingdom
£ 40.28
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Add to basketCondition: New. Print on Demand pp. 218.
Published by Packt Publishing Limited, 2018
ISBN 10: 1788392302 ISBN 13: 9781788392303
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
£ 36.56
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Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 526.