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

I hope that this chapter has shown you that deep learning is not just about computer vision and NLP problems! In this chapter, we covered using Keras to build auto-encoders and recommendation systems. We saw that auto-encoders can be used as a form of dimensionality reduction and, in their simplest forms with only one layer, they are similar to PCA. We used an auto-encoder model to create an anomaly detection system. If the reconstruction error in the auto-encoder model was over a threshold, then we marked that instance as a potential anomaly. Our second major example in this chapter built a recommendation system using Keras. We constructed a dataset of implicit ratings from transactional data and built a recommendation system. We demonstrated the practical application of this model by showing you how it could be used for cross-sell purposes.

In the next chapter, we will...