This book is about how to train and use deep learning models or deep neural networks in the R programming language and environment. This book is not intended to provide an in-depth theoretical coverage of deep neural networks, but it will give you enough background to help you understand their basics and use and interpret the results. This book will also show you some of the packages and functions available to train deep neural networks, optimize their hyperparameters to improve the accuracy of your model, and generate predictions or otherwise use the model you built. The book is intended to provide an easy-to-read coverage of the essentials in order to get going with real-life examples and applications.
Chapter 1, Getting Started with Deep Learning, shows how to get the R and H2O packages set up and installed on a computer or server along with covering all the basic concepts related to deep learning.
Chapter 2, Training a Prediction Model, covers how to build a shallow unsupervised neural network prediction model.
Chapter 3, Preventing Overfitting, explains different approaches that can be used to prevent models from overfitting the data in order to improve generalizability called regularization on unsupervised data.
Chapter 4, Identifying Anomalous Data, covers how to perform unsupervised deep learning in order to identify anomalous data, such as fraudulent activity or outliers.
Chapter 5, Training Deep Prediction Models, shows how to train deep neural networks to solve prediction and classification problems, such as image recognition.
Chapter 6, Tuning and Optimizing Models, explains how to adjust model tuning parameters to improve and optimize the accuracy and performance of deep learning models.
Appendix, Bibliography, contains the references for all the citations throughout the book.