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)

Summary

In this chapter, we used deep learning for image classification. We discussed the different layer types that are used in image classification: convolutional layers, pooling layers, dropout, dense layers, and the softmax activation function. We saw an R-Shiny application that shows how convolutional layers perform feature engineering on image data.

We used the MXNet deep learning library in R to create a base deep learning model which got 97.1% accuracy. We then developed a CNN deep learning model based on the LeNet architecture, which achieved over 98.3% accuracy on test data. We also used a slightly harder dataset (Fashion MNIST) and created a new model that achieved over 91% accuracy. This accuracy score was better than all of the other scores that used non-deep learning algorithms. In the next chapter, we will build on what we have covered and show you how we can take...