Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Scala for Machine Learning

You're reading from   Scala for Machine Learning Leverage Scala and Machine Learning to construct and study systems that can learn from data

Arrow left icon
Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783558742
Length 624 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Patrick R. Nicolas Patrick R. Nicolas
Author Profile Icon Patrick R. Nicolas
Patrick R. Nicolas
Arrow right icon
View More author details
Toc

Table of Contents (15) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Hello World! 3. Data Preprocessing 4. Unsupervised Learning 5. Naïve Bayes Classifiers 6. Regression and Regularization 7. Sequential Data Models 8. Kernel Models and Support Vector Machines 9. Artificial Neural Networks 10. Genetic Algorithms 11. Reinforcement Learning 12. Scalable Frameworks A. Basic Concepts Index

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: "Finally, the environment variables JAVA_HOME, PATH, and CLASSPATH have to be updated accordingly."

A block of code is set as follows:

[default]
val lsp = builder.model(lrJacobian)
                 .weight(wMatrix) 
                 .target(labels)

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

[default]
val lsp = builder.model(lrJacobian)
                 .weight(wMatrix)
                  .target(labels)

The source code block is described using a reference number embedded as a code comment:

[default]
val lsp = builder.model(lrJacobian)  //1
                 .weight(wMatrix)
                  .target(labels)

The reference number is used in the chapter as follows: "The model instance is initialized with the Jacobian matrix, lrJacobian (line 1)."

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

sbt/sbt assembly

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "The loss function is then known as the hinge loss."

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

Note

Mathematical formulas (optional to read) appear in a box like this

For the sake of readability, the elements of the Scala code that are not essential to the understanding of an algorithm such as class, variable, and method qualifiers and validation of arguments, exceptions, or logging are omitted. The convention for code snippets is detailed in the Format of code snippets section in Appendix A, Basic Concepts.

You will be provided with in-text citation of papers, conference, books, and instructional videos throughout the book. The sources are listed in the the Appendix B, References using in the following format:

[In-text citation]

For example, in the chapter, you will find an instance as follows:

This time around RSS increases with Conventions before reaching a maximum for Conventions > 60. This behavior is consistent with other findings [6:12].

The respective [source entry] is mentioned in Appendix B, References, as follows:

[6:12] Model selection and assessment H. Bravo, R. Irizarry, 2010, available at http://www.cbcb.umd.edu/~hcorrada/PracticalML/pdf/lectures/selection.pdf.

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime