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R Deep Learning Essentials

You're reading from   R Deep Learning Essentials A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781788992893
Length 378 pages
Edition 2nd Edition
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Authors (2):
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Joshua F. Wiley Joshua F. Wiley
Author Profile Icon Joshua F. Wiley
Joshua F. Wiley
Mark Hodnett Mark Hodnett
Author Profile Icon Mark Hodnett
Mark Hodnett
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning FREE CHAPTER 2. Training a Prediction Model 3. Deep Learning Fundamentals 4. Training Deep Prediction Models 5. Image Classification Using Convolutional Neural Networks 6. Tuning and Optimizing Models 7. Natural Language Processing Using Deep Learning 8. Deep Learning Models Using TensorFlow in R 9. Anomaly Detection and Recommendation Systems 10. Running Deep Learning Models in the Cloud 11. The Next Level in Deep Learning 12. Other Books You May Enjoy

Document classification

This chapter will be looking at text classification using Keras. The dataset we will use is included in the Keras library. As we have done in previous chapters, we will use traditional machine learning techniques to create a benchmark before applying a deep learning algorithm. The reason for this is to show how deep learning models perform against other techniques.

The Reuters dataset

We will use the Reuters dataset, which can be accessed through a function in the Keras library. This dataset has 11,228 records with 46 categories. To see more information about this dataset, run the following code:

library(keras)
?dataset_reuters

Although the Reuters dataset can be accessed from Keras, it is not in a format...

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