Book Image

R Deep Learning Essentials. - Second Edition

By : Mark Hodnett, Joshua F. Wiley
Book Image

R Deep Learning Essentials. - Second Edition

By: Mark Hodnett, Joshua F. Wiley

Overview of this book

Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.
Table of Contents (13 chapters)

Deploying TensorFlow models

Historically, one of the perceived disadvantages of using R for data science projects was the difficulty in deploying machine learning models built in R. This often meant that companies used R mainly as a prototyping tool to build models which were then rewritten in another language, such as Java and .NET. It is also one of the main reasons cited for companies switching to Python for data science as Python has more glue code, which allows it to interface with other programming languages.

Thankfully, this is changing. One interesting new product from RStudio, called RStudio Connect, allows companies to create a platform for sharing R-Shiny applications, reports in R Markdown, dashboards, and models. This allows companies to serve machine learning models using a REST interface.

The TensorFlow (and Keras) models we have created in this book can be deployed...