To get the most out of this book

The following are a few ideas for how you can get the most out of this book:

All examples in this book use R codes. So before getting started with it, you should havea good foundation in the R language. As per Confucius, "I hear and I forget. I see and I remember. I do and I understand." This is true for this book, too. A hands-on approach of working with the codes while going through the chapters will be very useful in understanding the deep learning models.

All the codes in this book were successfully run on a Mac computer that had 8 GB of RAM. However, if you are working with a much larger dataset compared to what has been used in this book for illustration purposes, more powerful computing resources may be required in order to develop deep learning models. It will also be helpful to have a good foundation in statistical methods.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packt.com.
2. Select the Support tab.
3. Click on Code Downloads.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for Mac
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Advanced-Deep-Learning-with-R. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781789538779_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "We store the accuracy and loss values while fitting the model in model_three."

A block of code is set as follows:

model %>% 
   compile(loss = 'binary_crossentropy', 
   optimizer = 'adam',
   metrics = 'accuracy')

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Recurrent neural networks (RNNs) are well suited to working with data involving such sequences."

Warnings or important notes appear like this.

Tips and tricks appear like this.