All the examples have been implemented using Deeplearning4j with some open source libraries in Java. To be more specific, the following API/tools are required:

• Java/JDK version 1.8
• Spark version 2.3.0
• Spark csv_2.11 version 1.3.0
• ND4j backend version nd4j-cuda-9.0-platform for GPU, otherwise nd4j-native
• ND4j version >=1.0.0-alpha
• DL4j version >=1.0.0-alpha
• Datavec version >=1.0.0-alpha
• Arbiter version >=1.0.0-alpha
• Logback version 1.2.3
• JavaCV platform version 1.4.1
• HTTP Client version 4.3.5
• Jfreechart 1.0.13
• Jcodec 0.2.3
• Eclipse Mars or Luna (latest) or Intellij IDEA
• Maven Eclipse plugin (2.9 or higher)
• Maven compiler plugin for Eclipse (2.3.2 or higher)
• Maven assembly plugin for Eclipse (2.4.1 or higher)

Regarding operating system: Linux distributions are preferable (including Debian, Ubuntu, Fedora, RHEL, CentOS). To be more specific, for example, for Ubuntu it is recommended to have a 14.04 (LTS) 64-bit (or later) complete installation or VMWare player 12 or Virtual box. You can run Spark jobs on Windows (XP/7/8/10) or Mac OS X (10.4.7+).

Regarding hardware configuration: A machine or server having core i5 processor, about 100 GB disk space, and at least 16 GB RAM. In addition, an Nvidia GPU driver has to be installed with CUDA and CuDNN configured if you want to perform the training on GPU. Enough storage for running heavy jobs is needed (depending on the dataset size you will be handling), preferably at least 50 GB of free disk storage (for standalone and for SQL warehouse).

You can download the example code files for this book from your account at www.packtpub.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.packtpub.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
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/Java-Deep-Learning-Projects. 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!

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/JavaDeepLearningProjects_ColorImages.pdf.

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: "Then, I unzipped and copied each .csv file into a folder called label."

A block of code is set as follows:

<properties>
  <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
  <java.version>1.8</java.version>
</properties>

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

<properties>
  <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
  <java.version>1.8</java.version>
</properties>

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: "We then read and process images into PhotoID | Vector map"