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 really covered a lot in this chapter! We built a fairly complex traditional NLP example that had many hyperparameters, as well as training it on several machine learning algorithms. It achieved a reputable result of getting 95.24% accuracy. However, when we looked into traditional NLP in more detail, we found that it had some major problems: it requires non-trivial feature engineering, it creates sparse high-dimensional data frames, and it may require discarding a substantial amount of data before machine learning.

In comparison, the deep learning approach uses word vectors or embeddings, which are much more efficient and do not require preprocessing. We ran through a number of deep learning approaches, including 1D convolutional layers, Recurrent Neural Networks, GRUs, and LSTM. We finally combined the two best previous approaches into one approach in our final model...