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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

How GANs can fail

Training a GAN is a tricky thing, to say the least. There are an amazing number of ways one fail at training a GAN. In fact, in writing this chapter, I found myself expanding the vocabulary of my profanity vector significantly while also spending a small fortune on cloud GPU time! Before I show you two working GANs later in the chapter, let's consider what could break and how we might be able to fix those things.

Stability

Training a GAN requires a careful balancing act between the discriminator and generator. The discriminator and generator are both fighting against each other for deep network supremacy. On the other hand, they also need each other to learn and grow. In order for this to work, neither...

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