Scala for Machine Learning(2nd) 无水印pdf

Scala

Machine

Learning

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Scala for Machine Learning Second

Edition

retrieval system, or transmitted in any form or by any means, without the

prior written permission of the publisher, except in the case of brief

quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the

accuracy of the information presented. However, the information contained in

this book is sold without warranty, either express or implied. Neither the

author, nor Packt Publishing, and its dealers and distributors will be held

liable for any damages caused or alleged to be caused direFmaptctly or

indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all

of the companies and products mentioned in this book by the appropriate use

of capitals. However, Packt Publishing cannot guarantee the accuracy of this

information.

First published: December 2015

Second edition: September 2017

Production reference: 1190917

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78712-238-3

www.packtpub.com

Table of Contents
Scala for Machine Learning Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
eBooks, discount offers, and more
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Getting Started
Mathematical notations for the curious
Why machine learning?
Classification
Prediction
Optimization
Regression
Why Scala?
Scala as a functional language
Abstraction
Higher kinded types
Functors

Monads
Scala as an object oriented language
Scala as a scalable language
Model categorization
Taxonomy of machine learning algorithms
Unsupervised learning
Clustering
Dimension reduction
Supervised learning
Generative models
Discriminative models
Semi-supervised learning
Reinforcement learning
Leveraging Java libraries
Tools and frameworks
Java
Scala
Eclipse Scala IDE
IntelliJ IDEA Scala plugin
Simple build tool
Apache Commons Math
Description
Licensing
Installation
JFreeChart
Description
Licensing
Installation
Other libraries and frameworks
Source code
Convention
Context bounds
Presentation
Primitives and implicits
Immutability
Let's kick the tires
Writing a simple workflow

Step 1 – scoping the problem
Step 2 – loading data
Step 3 – preprocessing data
Immutable normalization
Step 4 – discovering patterns
Analyzing data
Plotting data
Visualizing model features
Visualizing label
Step 5 – implementing the classifier
Selecting an optimizer
Training the model
Classifying observations
Step 6 – evaluating the model
Summary
2. Data Pipelines
Modeling
What is a model?
Model versus design
Selecting features
Extracting features
Defining a methodology
Monadic data transformation
Error handling
Monads to the rescue
Implicit models
Explicit models
Workflow computational model
Supporting mathematical abstractions
Step 1 – variable declaration
Step 2 – model definition
Step 3 – instantiation
Composing mixins to build workflow
Understanding the problem
Defining modules
Instantiating the workflow
Modularizing