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)

TensorFlow models

In this section, we will use TensorFlow to build some machine learning models. First, we will build a simple linear regression model and then a convolutional neural network model, similar to what we have seen in Chapter 5, Image Classification Using Convolutional Neural Networks.

The following code loads the TensorFlow library. We can confirm it loaded successfully by setting and accessing a constant string value:

> library(tensorflow)

# confirm that TensorFlow library has loaded
> sess=tf$Session()
> hello_world <- tf$constant('Hello world from TensorFlow')
> sess$run(hello_world)
b'Hello world from TensorFlow'

Linear regression using TensorFlow

In this first Tensorflow example...