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Paperback. Condizione: Good. No Jacket. Former library book; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.
Da: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
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Da: Lakeside Books, Benton Harbor, MI, U.S.A.
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
Condizione: New.
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition.
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Condizione: New.
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 39,99
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Da: Chiron Media, Wallingford, Regno Unito
EUR 37,57
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 38,46
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Da: Revaluation Books, Exeter, Regno Unito
EUR 45,54
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Aggiungi al carrelloPaperback. Condizione: Brand New. 285 pages. 9.00x6.00x0.75 inches. In Stock.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Condizione: New. 2nd ed. edition NO-PA16APR2015-KAP.
ISBN 10: 1484283767 ISBN 13: 9781484283769
Da: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
Condizione: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.
Paperback or Softback. Condizione: New. Applied Deep Learning with Tensorflow 2: Learn to Implement Advanced Deep Learning Techniques with Python. Book.
Da: Majestic Books, Hounslow, Regno Unito
EUR 62,13
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 56,15
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 61,72
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 61,23
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Da: Chiron Media, Wallingford, Regno Unito
EUR 60,04
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
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EUR 48,14
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow.Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models.Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 304 pp. Englisch.
Da: preigu, Osnabrück, Germania
EUR 42,05
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Advanced Applied Deep Learning | Convolutional Neural Networks and Object Detection | Umberto Michelucci | Taschenbuch | xviii | Englisch | 2019 | APRESS | EAN 9781484249758 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Da: medimops, Berlin, Germania
EUR 15,31
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Aggiungi al carrelloCondizione: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.
Da: preigu, Osnabrück, Germania
EUR 58,60
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Applied Deep Learning with TensorFlow 2 | Learn to Implement Advanced Deep Learning Techniques with Python | Umberto Michelucci | Taschenbuch | xxviii | Englisch | 2022 | Apress | EAN 9781484280195 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 48,14
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow.Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level.What You Will LearnSee how convolutional neural networks and object detection workSave weights and models on diskPause training and restart it at a later stage Use hardware acceleration (GPUs) in your codeWork with the Dataset TensorFlow abstraction and use pre-trained models and transfer learningRemove and add layers to pre-trained networks to adapt them to your specific projectApply pre-trained models such as Alexnet and VGG16 to new datasetsWho This Book Is ForScientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected. 304 pp. Englisch.
Da: moluna, Greven, Germania
EUR 40,39
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The first book with extensive examples of advanced deep learning techniques including CNNUses real-life datasets in the application of advanced techniques Guides you from easier examples to more advanced tech.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 50,08
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow.Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level.What You Will LearnSee how convolutional neural networks and object detection workSave weights and models on diskPause training and restart it at a later stage Use hardware acceleration (GPUs) in your codeWork with the Dataset TensorFlow abstraction and use pre-trained models and transfer learningRemove and add layers to pre-trained networks to adapt them to your specific projectApply pre-trained models such as Alexnet and VGG16 to new datasetsWho This Book Is ForScientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.