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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

Product type Paperback

Published in Mar 2018

Publisher Packt

ISBN-13 9781788837996

Length 272 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Author (1):

Mike Bernico

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Table of Contents (22) Chapters

Title Page

Copyright and Credits

Dedication

Packt Upsell

Foreword

Contributors

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

1. Other Books You May Enjoy

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Index

Building a reinforcement learning agent in Keras

Good news, we're finally ready to start coding. In this section, I'm going to demonstrate two Keras-RL agents called CartPole and Lunar Lander. I've chosen these examples because they won't consume your GPU and your cloud budget to run. They can be easily extended to Atari problems, and I've included one of those as well in the book's Git repository. You can find all this code in the Chapter12 folder, as usual. Let's talk quickly about these two environments:

CartPole: The CartPole environment consists of a pole, balanced on a cart. The agent has to learn how to balance the pole vertically, while the cart underneath it moves. The agent is given the position of the cart, the velocity of the cart, the angle of the pole, and the rotational rate of the pole as inputs. The agent can apply a force on either side of the cart. If the pole falls more than 15 degrees from vertical, it's game over for our agent.
Lunar Lander: The Lunar Lander environment...

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Authors (1)

Mike Bernico

Mike Bernico

Mike Bernico is a Lead Data Scientist at State Farm Mutual Insurance Companies. He also works as an adjunct for the University of Illinois at Springfield, where he teaches Essentials of Data Science, and Advanced Neural Networks and Deep Learning. Mike earned his MSCS from the University of Illinois at Springfield. He's an advocate for open source software and the good it can bring to the world. As a lifelong learner with umpteen hobbies, Mike also enjoys cycling, travel photography, and wine making.

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