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, the reader has seen some advanced deep learning techniques. First, we looked at some image classification models and looked at some historical models. Next, we loaded an existing model with pre-trained weights into R and used it to classify a new image. We looked at transfer learning, which allows us to reuse an existing model as a base on which to build a deep learning model for new data. We built an image classifier model that could train on image files. This model also showed us how to use data augmentation and callbacks, which are used in many deep learning models. Finally, we demonstrated how we can build a model in R and create a REST endpoint for a prediction API that can be used from other applications or across the web.

We have come to the end of the book, and I really hope it was useful to you. R is a great language for data science and I believe...