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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

Summary

Stanford teaches an entire course only on reinforcement learning. It would have been possible to write an entire book just on reinforcement learning, and in fact that has been done many times. My hope for this chapter is to show you just enough to start you on your way towards solving reinforcement learning problems.

As I solved the Lunar Lander problem, it was easy to let my mind wander from toy problems to actual space exploration with deep Q network-powered agents. I hope this chapter does the same for you.

In the next chapter, I'll show you one last use of Deep Neural networks where we will look at networks that can generate new images, data points, and and even music, called Generative Adversarial Networks.

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