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Deep Learning Quick Reference

You're reading from   Deep Learning Quick Reference Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788837996
Length 272 pages
Edition 1st Edition
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Author (1):
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Mike Bernico Mike Bernico
Author Profile Icon Mike Bernico
Mike Bernico
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Table of Contents (15) Chapters Close

Preface 1. The Building Blocks of Deep Learning FREE CHAPTER 2. Using Deep Learning to Solve Regression Problems 3. Monitoring Network Training Using TensorBoard 4. Using Deep Learning to Solve Binary Classification Problems 5. Using Keras to Solve Multiclass Classification Problems 6. Hyperparameter Optimization 7. Training a CNN from Scratch 8. Transfer Learning with Pretrained CNNs 9. Training an RNN from scratch 10. Training LSTMs with Word Embeddings from Scratch 11. Training Seq2Seq Models 12. Using Deep Reinforcement Learning 13. Generative Adversarial Networks 14. Other Books You May Enjoy

Transfer learning in Keras

Unlike in other examples in this book, here we will need to cover both the target domain problem, the source domain problem, and the network architecture we're using. We will start with an overview of the target domain, which is the problem we're trying to solve. Then we will cover the source domain our network was originally trained on and briefly cover the network architecture we will be using. Then, we will spend the rest of the chapter wiring the problem together. We need to consider both domains separately because their size and similarity are closely related to network performance. The closer the target and source are in type, the better the results.

Target domain overview

In this...

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