Preface

Deep learning finds practical applications in several domains and R is a preferred language to design and deploy deep learning models.

This Learning Path introduces you to the basics of deep learning and teaches you to build a neural network model from scratch. As you make your way through the concepts, you’ll explore deep learning libraries and create deep learning models for a variety of problems, such as 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. Before it ends, this Learning Path teaches you advanced topics, such as model optimization, overfitting, and data augmentation. Through real-world projects, you’ll learn how to train convolutional neural networks, recurrent neural networks, and LSTMs in R.

By the end of this Learning Path, you’ll have a better understanding of deep learning concepts and will be able to implement deep learning concepts in your research work or projects.

This Learning Path includes content from the following Packt products:

• R Deep Learning Essentials - Second Edition by Joshua F. Wiley and Mark Hodnett
• R Deep Learning Projects by Yuxi (Hayden) Liu and Pablo Maldonado