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

This chapter showed how to get started building and training neural networks to classify data, including image recognition and physical activity data. We looked at packages that can visualize a neural network and we created a number of models to perform classification on data with 10 different categories. Although we only used some neural network packages rather than deep learning packages, our models took a long time to train and we had issues with overfitting.

Some of the basic neural network models in this chapter took a long time to train, even though we did not use all the data available. For the MNIST data, we used approx. 8,000 rows for our binary classification task and only 6,000 rows for our multi-classification task. Even so, one model took almost an hour to train. Our deep learning models will be much more complicated and should be able to process millions...