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.

**What this book covers**

** 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.

**What you need for this book**

You do not need much to use for this book. The main piece of software that you need is R, which is open source and runs on Windows, Mac OS, and many varieties of Linux. You will also need a recent version of Java. Once you have R and Java installed, you will need to install some R packages, all of which work on every major operating system. Perhaps, the more challenging requirement is that, for any real deep learning application, and even to explore quite small examples, modern hardware is required. For this book, I primarily used a desktop with an Intel Xeon E5-2670 v2 running at 2.50 GHz (10 physical cores, 20 logical cores), with 32 GB of memory, and a Samsung 850 PRO 512GB SSD. You do not necessarily need an equivalent system, but I found that running some examples on a latest laptop with 16 GB of memory and a dual core i7 processor is time consuming.

**Who this book is for**

This book caters to aspiring data scientists who are well-versed with machine learning concepts with R and are looking to explore the deep learning paradigm using the packages available in R. You should have a fundamental understanding of the R language and be comfortable with statistical algorithms and machine learning techniques, but you do not need to be well-versed with deep learning concepts.

**Conventions**

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning. Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: “Of course, we cannot actually use the library() function until we have installed the packages.”