To get the most out of this book

This book is dedicated to developers, data analysts, and deep learning enthusiasts who do not have much background with complex numerical computations but want to know what deep learning is. A strong understanding of Scala and its functional programming concepts is recommended. Some basic understanding and high-level knowledge of Spark ML, H2O, Zeppelin, DeepLearning4j, and MXNet would act as an added advantage in order to grasp this book. Additionally, basic know-how of build tools such as Maven and SBT is assumed.

All the examples have been implemented using Scala on an Ubuntu 16.04 LTs 64-bit and Windows 10 64-bit. You will also need the following (preferably the latest versions):

• Apache Spark 2.0.0 (or higher)
• MXNet, Zeppelin, DeepLearning4j, and H2O (see the details in the chapter and in the supplied pom.xml files)
• Hadoop 2.7 (or higher)
• Java (JDK and JRE) 1.7+/1.8+
• Scala 2.11.x (or higher)
• Eclipse Mars or Luna (latest) with Maven plugin (2.9+), Maven compiler plugin (2.3.2+), and Maven assembly plugin (2.4.1+)
• IntelliJ IDE
• SBT plugin and Scala Play Framework installed

A computer with at least a Core i3 processor, Core i5 (recommended), or Core i7 (to get the best results) is needed. However, multicore processing will provide faster data processing and scalability. At least 8 GB RAM is recommended for standalone mode; use at least 32 GB RAM for a single VM and higher for a cluster. You should have enough storage for running heavy jobs (depending on the dataset size you will be handling); preferably, at least 50 GB of free disk storage (for standalone and for SQL Warehouse).

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

Download the example code files

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/Scala-Machine-Learning-Projects. 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: http://www.packtpub.com/sites/default/files/downloads/ScalaMachineLearningProjects_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: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

A block of code is set as follows:

val cv = new CrossValidator()
      .setEstimator(pipeline)
      .setEvaluator(new RegressionEvaluator)
      .setEstimatorParamMaps(paramGrid)
      .setNumFolds(numFolds)

Scala functional code blocks look as follows:

 def variantId(genotype: Genotype): String = {
      val name = genotype.getVariant.getContigName
      val start = genotype.getVariant.getStart
      val end = genotype.getVariant.getEnd
      s"$name:$start:$end"
  }

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

var paramGrid = new ParamGridBuilder()
      .addGrid(dTree.impurity, "gini" :: "entropy" :: Nil)
      .addGrid(dTree.maxBins, 3 :: 5 :: 9 :: 15 :: 23 :: 31 :: Nil)
      .addGrid(dTree.maxDepth, 5 :: 10 :: 15 :: 20 :: 25 :: 30 :: Nil)
      .build()

Any command-line input or output is written as follows:

$ sudo mkdir Bitcoin
$ cd Bitcoin

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: "Select System info from the Administration panel."

Warnings or important notes appear like this.

Tips and tricks appear like this.