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TensorFlow 1.x Deep Learning Cookbook

You're reading from   TensorFlow 1.x Deep Learning Cookbook Over 90 unique recipes to solve artificial-intelligence driven problems with Python

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781788293594
Length 536 pages
Edition 1st Edition
Languages
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Authors (2):
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Dr. Amita Kapoor Dr. Amita Kapoor
Author Profile Icon Dr. Amita Kapoor
Dr. Amita Kapoor
Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
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Toc

Table of Contents (15) Chapters Close

Preface 1. TensorFlow - An Introduction 2. Regression FREE CHAPTER 3. Neural Networks - Perceptron 4. Convolutional Neural Networks 5. Advanced Convolutional Neural Networks 6. Recurrent Neural Networks 7. Unsupervised Learning 8. Autoencoders 9. Reinforcement Learning 10. Mobile Computation 11. Generative Models and CapsNet 12. Distributed TensorFlow and Cloud Deep Learning 13. Learning to Learn with AutoML (Meta-Learning) 14. TensorFlow Processing Units

Creating a DeepDream network

Google trained a neural network on ImageNet for the Large Scale Visual Recognition Challenge (ILSVRC) in 2014 and made it open source in July 2015. The original algorithm is presented in Going Deeper with Convolutions, Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke e Andrew Rabinovich (https://arxiv.org/abs/1409.4842) . The network learned a representation of each image. The lower layers learned low-level features, such as lines and edges, while the higher layers learned more sophisticated patterns such as eyes, noses, mouths, and so on. Therefore, if we try to represent a higher level in the network, we will see a mix of different features extracted from the original ImageNet such as the eyes of a bird and the mouth of a dog. With this in mind, if we take a new image and try...

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