Book Image

Deep Learning with R for Beginners

By : Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado
Book Image

Deep Learning with R for Beginners

By: Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado

Overview of this book

Deep learning has a range of practical applications in several domains, while R is the preferred language for designing and deploying deep learning models. This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The Learning Path will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R. By the end of this Learning Path, you’ll be well-versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Warm-up – data exploration


Let's get things moving with a tiny example. Let's look at this tiny reviews corpus:

text <- c("The food is typical Czech, and the beer is good. The service is quick, if short and blunt, and the waiting on staff could do with a bit of customer service training",
          "The food was okay. Really not bad, but we had better",
          "A venue full of locals. No nonsense, no gimmicks. Only went for drinks which were good and cheap. People friendly enough.",
          "Great food, lovely staff, very reasonable prices considering the location!")

We will do some simple analysis here, which will help us appreciate some of the subtleties of sentiment analysis.

Working with tidy text

For this, we will use the tidytext package. This package is built on the philosophy of tidy data, introduced by Hadley Wickham in his 2014 paper (https://www.jstatsoft.org/article/view/v059i10). A dataset is tidy if the following three conditions are satisfied:

  • Each variable is a column
  • Each...