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Verlag: Pearson Education, Limited, 1991
ISBN 10: 0201523760ISBN 13: 9780201523768
Anbieter: Better World Books, Mishawaka, IN, USA
Buch
Zustand: Good. Book&Disk. Used book that is in clean, average condition without any missing pages.
Verlag: Pearson Education, Limited, 1991
ISBN 10: 0201523760ISBN 13: 9780201523768
Anbieter: Better World Books, Mishawaka, IN, USA
Buch
Zustand: Very Good. Book&Disk. Used book that is in excellent condition. May show signs of wear or have minor defects.
Verlag: Elsevier Science & Technology Books, 1990
ISBN 10: 0122286405ISBN 13: 9780122286407
Anbieter: Better World Books, Mishawaka, IN, USA
Buch
Zustand: Good. Used book that is in clean, average condition without any missing pages.
Verlag: Elsevier Science & Technology Books, 1990
ISBN 10: 0122286405ISBN 13: 9780122286407
Anbieter: Better World Books, Mishawaka, IN, USA
Buch
Zustand: Good. Former library book; may include library markings. Used book that is in clean, average condition without any missing pages.
Verlag: Morgan Kaufmann, 1996
ISBN 10: 0126791708ISBN 13: 9780126791709
Anbieter: WorldofBooks, Goring-By-Sea, WS, Vereinigtes Königreich
Buch
Paperback. Zustand: Good. The book has been read but remains in clean condition. All pages are intact and the cover is intact. Some minor wear to the spine.
Verlag: Prentice Hall, 1991
ISBN 10: 0201523760ISBN 13: 9780201523768
Anbieter: Reuseabook, Gloucester, GLOS, Vereinigtes Königreich
Buch
Hardcover. Zustand: Used; Very Good. Dispatched, from the UK, within 48 hours of ordering. Though second-hand, the book is still in very good shape. Minimal signs of usage may include very minor creasing on the cover or on the spine.
Verlag: -, 1996
ISBN 10: 0126791708ISBN 13: 9780126791709
Anbieter: AwesomeBooks, Wallingford, Vereinigtes Königreich
Buch
Paperback. Zustand: Very Good. Applying Neural Networks: A Practical Guide This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping.
Verlag: Prentice Hall, 1991
ISBN 10: 0201523760ISBN 13: 9780201523768
Anbieter: Anybook.com, Lincoln, Vereinigtes Königreich
Buch
Zustand: Poor. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In poor condition, suitable as a reading copy. Dust Jacket in good condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,800grams, ISBN:9780201523768.
Verlag: ACADEMIC PR INC, 1996
ISBN 10: 0126791708ISBN 13: 9780126791709
Anbieter: Buchpark, Trebbin, Deutschland
Buch
Zustand: Gut. Zustand: Gut - Gebrauchs- und Lagerspuren. Fehlt: CD. | Seiten: 303 | Sprache: Englisch.
Verlag: Data Science, 2019
ISBN 10: 390333118XISBN 13: 9783903331181
Anbieter: Buchpark, Trebbin, Deutschland
Buch
Zustand: Sehr gut. Zustand: Sehr gut - Gepflegter, sauberer Zustand.
Verlag: Data Science, 2019
ISBN 10: 3903331724ISBN 13: 9783903331723
Anbieter: Buchpark, Trebbin, Deutschland
Buch
Zustand: Sehr gut. Zustand: Sehr gut - Gepflegter, sauberer Zustand. | Seiten: 292.
Verlag: Data Science, 2019
ISBN 10: 3903331724ISBN 13: 9783903331723
Anbieter: Buchpark, Trebbin, Deutschland
Buch
Zustand: Wie neu. Zustand: Wie neu | Seiten: 292.
Verlag: Springer International Publishing, 2018
ISBN 10: 3319861905ISBN 13: 9783319861906
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch
Taschenbuch. Zustand: 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.
Verlag: Springer International Publishing, 2017
ISBN 10: 331957549XISBN 13: 9783319575490
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch
Buch. Zustand: 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.