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

This chapter presented a brief introduction to neural networks and deep neural networks. Using multiple hidden layers, deep neural networks have been a revolution in machine learning. They consistently outperform other machine learning tasks, especially in areas such as computer vision, natural-language processing, and speech-recognition.

The chapter also looked at some of the theory behind neural networks, the difference between shallow neural networks and deep neural networks, and some of the misconceptions that currently exist concerning deep learning.

We closed this chapter with a discussion on how to set up R and the importance of using a GUI (RStudio). This section discussed the deep learning libraries available in R (MXNet, Keras, and TensorFlow), GPUs, and reproducibility.

In the next chapter, we will begin to train neural networks and generate our own predictions.