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Hands-On Deep Learning with R

You're reading from   Hands-On Deep Learning with R A practical guide to designing, building, and improving neural network models using R

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781788996839
Length 330 pages
Edition 1st Edition
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Authors (2):
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Rodger Devine Rodger Devine
Author Profile Icon Rodger Devine
Rodger Devine
Michael Pawlus Michael Pawlus
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Michael Pawlus
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Table of Contents (16) Chapters Close

Preface 1. Section 1: Deep Learning Basics
2. Machine Learning Basics FREE CHAPTER 3. Setting Up R for Deep Learning 4. Artificial Neural Networks 5. Section 2: Deep Learning Applications
6. CNNs for Image Recognition 7. Multilayer Perceptron for Signal Detection 8. Neural Collaborative Filtering Using Embeddings 9. Deep Learning for Natural Language Processing 10. Long Short-Term Memory Networks for Stock Forecasting 11. Generative Adversarial Networks for Faces 12. Section 3: Reinforcement Learning
13. Reinforcement Learning for Gaming 14. Deep Q-Learning for Maze Solving 15. Other Books You May Enjoy

Preparing and processing data

For our first task, we will use the tic-tac-toe dataset from the ReinforcementLearning package. In this case, the dataset is built for us; however, we will investigate how it is made to understand how to get data into the proper format for reinforcement learning:

  1. First, let's load the tic-tac-toe data. To load the dataset, we first load the ReinforcementLearning library and then call the data function with "tictactoe" as the argument. We load our data by running the following code:
library(ReinforcementLearning)

data("tictactoe")

After running these lines, you will see the data object in the data Environment pane. Its current type is <Promise>; however, we will change that in the next step to see what is contained in this object. For now, your Environment pane will look like the following screenshot:

  1. Now...
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