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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

Using a pretrained VGG16 net for transfer learning

In this recipe, we will discuss transfer learning, a very powerful deep learning technique that has many applications in different domains. The intuition is very simple and can be explained with an analogy. Suppose you want to learn a new language, say Spanish, then it could be useful to start from what you already know in a different language, say English.

Following this line of thinking, computer vision researchers now commonly use pretrained CNNs to generate representations for novel tasks, where the dataset may not be large enough to train an entire CNN from scratch. Another common tactic is to take the pretrained ImageNet network and then to fine-tune the entire network to the novel task. The example proposed here has been inspired by Francois Chollet in a famous blog posting for Keras. (https://blog.keras.io/building-powerful...

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