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Ajouter au panierEtat : 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.
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Ajouter au panierEtat : Brand New. New.SoftCover International edition. Different ISBN and Cover image but contents are same as US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
Vendeur : California Books, Miami, FL, Etats-Unis
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Ajouter au panierPaperback or Softback. Etat : New. Applied Deep Learning with Tensorflow 2: Learn to Implement Advanced Deep Learning Techniques with Python 1.56. Book.
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Ajouter au panierPaperback. Etat : Brand New. 285 pages. 9.00x6.00x0.75 inches. In Stock.
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Vendeur : California Books, Miami, FL, Etats-Unis
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Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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ISBN 10 : 1484283767 ISBN 13 : 9781484283769
Vendeur : Romtrade Corp., STERLING HEIGHTS, MI, Etats-Unis
EUR 30,08
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Ajouter au panierEtat : 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.
Edité par Apress, Apress Sep 2019, 2019
ISBN 10 : 1484249755 ISBN 13 : 9781484249758
Langue: anglais
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 48,14
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Ajouter au panierTaschenbuch. Etat : 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.
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
EUR 50,11
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Vendeur : Books Puddle, New York, NY, Etats-Unis
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Ajouter au panierEtat : New. 2nd ed. edition NO-PA16APR2015-KAP.
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 64,42
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Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Vendeur : Lakeside Books, Benton Harbor, MI, Etats-Unis
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Ajouter au panierEtat : New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books!
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Vendeur : moluna, Greven, Allemagne
EUR 40,39
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Ajouter au panierEtat : 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.
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EUR 46,31
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Ajouter au panierPaperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 480.
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 48,14
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Ajouter au panierTaschenbuch. Etat : 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.
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 50,60
Autre deviseQuantité disponible : 1 disponible(s)
Ajouter au panierTaschenbuch. Etat : 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.