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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 FREE CHAPTER 2. Regression 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

Advanced Convolutional Neural Networks

In this chapter, we will discuss how Convolutional Neural Networks (CNNs) are used for deep learning in domains other than images. Our attention will be initially devoted to text analysis and natural language processing (NLP). In this chapter, we will present a number of recipes for:

  • Creating a ConvNet for sentiment analysis
  • Inspecting what filters a VGG pre-built network has learned
  • Classifying images with VGGNet, ResNet, Inception, and Xception
  • Recycling pre-built deep learning models for extracting features
  • Very deep Inception-v3 net used for transfer learning
  • Generating music with dilated ConvNets, WaveNet, and NSynth
  • Answering questions about images (Visual Q&A)
  • Classifying videos with pre-trained nets in six different ways
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