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

We have covered a lot of options for training deep learning models in this chapter! We discussed options for running it locally and showed the importance of having a GPU card. We used the three main cloud providers to train deep learning models in R on the cloud. Cloud computing is a fantastic resource we gave an example of a super-computer costing $149,000. A few years ago, such a resource would have been out of reach for practically everyone, but now thanks to cloud computing, you can rent a machine like this on an hourly basis.

For AWS, Azure, and Paperspace, we installed MXNet on the cloud resources, giving us the option of which deep learning library to use. I encourage you to use the examples in the other chapters in this book and try all the different cloud providers here. It is amazing to think that you could do so and your total cost could be less than...