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You're reading from Advanced Deep Learning with R Become an expert at designing, building, and improving advanced neural network models using R

Product type Paperback

Published in Dec 2019

Publisher Packt

ISBN-13 9781789538779

Length 352 pages

Edition 1st Edition

Languages

Tools

H2O

Concepts

Deep Learning

Author (1):

Bharatendra Rai

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Table of Contents (20) Chapters

Preface

1. Section 1: Revisiting Deep Learning Basics

2. Revisiting Deep Learning Architecture and Techniques FREE CHAPTER

3. Section 2: Deep Learning for Prediction and Classification

4. Deep Neural Networks for Multi-Class Classification

5. Deep Neural Networks for Regression

6. Section 3: Deep Learning for Computer Vision

7. Image Classification and Recognition

8. Image Classification Using Convolutional Neural Networks

9. Applying Autoencoder Neural Networks Using Keras

10. Image Classification for Small Data Using Transfer Learning

11. Creating New Images Using Generative Adversarial Networks

12. Section 4: Deep Learning for Natural Language Processing

13. Deep Networks for Text Classification

14. Text Classification Using Recurrent Neural Networks

15. Text classification Using Long Short-Term Memory Network

16. Text Classification Using Convolutional Recurrent Neural Networks

17. Section 5: The Road Ahead

18. Tips, Tricks, and the Road Ahead

19. Other Books You May Enjoy

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Preparing data for model building

In this chapter, we'll be using the Internet Movie Database (IMDb) movie reviews text data that's available in the Keras package. Note that there is no need to download this data from anywhere as it can be easily accessed from the Keras library using code that we will discuss soon. In addition, this dataset is preprocessed so that text data is converted into a sequence of integers. We cannot use text data directly for model building, and such preprocessing of text data into a sequence of integers is necessary before the data can be used as input for developing deep learning networks.

We will start by loading the imdb data using the dataset_imdb function, where we will also specify the number of most frequent words as 500 using num_words. Then, we'll split the imdb data into train and test datasets. Let's take a look at the...

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Authors (1)

Rai

Rahul Rai is a technology consultant based in Sydney, Australia with over nine years of professional experience. He has been at the forefront of cloud consulting for government organizations and businesses around the world. Rahul has been working on Microsoft Azure since the service was in its infancy, delivering an ITSM tool built for and on Azure in 2008. Since then, Rahul has played the roles of a developer, a consultant, and an architect for nterprises ranging from small start-ups to multinational corporations. He worked for over five years with Microsoft Services with diverse teams to deliver innovative solutions on Microsoft Azure. In Microsoft, Rahul was a subject matter expert in Microsoft cloud technologies. Rahul has also worked as a cloud solution architect for Microsoft, for which he worked closely with some established Microsoft partners to drive joint customer transformations to cloud based architectures.

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