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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

Convolutional Neural Networks

Convolutional Neural Networks (CNNs or sometimes referred to as ConvNets) are fascinating. Over a short period of time, they became a disruptive technology, breaking all the state-of-the-art results in multiple domains from text, to video, to speech--going well beyond their original use for image processing. In this chapter, we will present a number of recipes as follows:

  • Creating a ConvNet to classify handwritten MNIST numbers
  • Creating a ConvNet to classify CIFAR-10
  • Transferring style with VGG19 for image repainting
  • Using a pretrained VGG16 net for transfer learning
  • Creating a DeepDream network
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