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Advanced Applied Deep Learning by Michelucci Umberto (30 results)
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Add to basketCondition: 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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Paperback or Softback. Condition: New. Applied Deep Learning with Tensorflow 2: Learn to Implement Advanced Deep Learning Techniques with Python 1.56. Book.
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Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection
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Advanced Applied Deep Learning : Convolutional Neural Networks and Object Detection
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Advanced Applied Deep Learning : Convolutional Neural Networks and Object Detection
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International EditionApplied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python
ISBN 10: 1484283767 ISBN 13: 9781484283769
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Applied Deep Learning With Tensorflow 2 : Learn to Implement Advanced Deep Learning Techniques With Python
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Applied Deep Learning With Tensorflow 2 : Learn to Implement Advanced Deep Learning Techniques With Python
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Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python
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Advanced Applied Deep Learning
Published by Apress, Apress Sep 2019, 2019
ISBN 10: 1484249755 ISBN 13: 9781484249758
Language: English
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Add to basketTaschenbuch. Condition: 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.
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Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection
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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 480.
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Advanced Applied Deep Learning
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Add to basketTaschenbuch. Condition: 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.
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Add to basketTaschenbuch. Condition: 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.
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US$ 48.83
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Add to basketCondition: 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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Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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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 773.
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects.This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks.All the code presented in the book will be available in the form of Jupyter not Elektronisches Buch which would allow you to try out all examples and extend them in interesting ways. A companion online book is available with the complete code for all examples discussed in the book and additional material more related to TensorFlow and Keras. All the code will be available in Jupyter notebook format and can be openeddirectly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally.You will:-Understand the fundamental concepts of how neural networks work-Learn the fundamental ideas behind autoencoders and generative adversarial networks-Be able to try all the examples with complete code examples that you can expand for your own projects-Have available a complete online companion book with examples and tutorials.This book is for:Readers with an intermediate understanding of machine learning, linear algebra, calculus, and basic Python programming.
