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)

Use case – building and applying a neural network

To close the chapter, we will discuss a more realistic use case for neural networks. We will use a public dataset by Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J. L. (2013) that uses smartphones to track physical activity. The data can be downloaded at https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones. The smartphones had an accelerometer and gyroscope from which 561 features from both time and frequency were used.

The smartphones were worn during walking, walking upstairs, walking downstairs, standing, sitting, and lying down. Although this data came from phones, similar measures could be derived from other devices designed to track activity, such as various fitness-tracking watches or bands. So this data can be useful if we want to sell devices and have them automatically...