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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
Author Profile Icon Michael Pawlus
Michael Pawlus
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Toc

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 preprocessing a dataset

For this chapter, we will use a small subset of images from the labeled faces in the wild dataset. Specifically, we will use images of former United States president George W. Bush, since this is the image object that occurs most often in the dataset. Using the following code, we will bring in the image data and convert it to a format that can be inputted into our model. We start by loading the libraries and data files required.

Loading the libraries and data files

We will begin by using the following steps:

  1. First, we load all the libraries that we will use. We will use just one function from each of these libraries but we need each one to get our data in the proper format. The jpeg library...
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