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Scala Applied Machine Learning .pdf
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2017-10-22
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Java 9 Concurrency Cookbook Second Edition Java 9 Concurrency Cookbook Second Edition Java 9 Concurrency Cookbook Second Edition
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Scala:Applied Machine Learning
Download from finelybook www.finelybook.com
2
Table of Contents
Scala:Applied Machine Learning
Scala:Applied Machine Learning
Credits
Preface
What this learning path covers
What you need for this learning path
Module 1
Installing the JDK
Installing and using SBT
Module 2
Module 3
Who this learning path is for
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
I. Module 1
1. Scala and Data Science
Data science
Programming in data science
Why Scala?
Static typing and type inference
Scala encourages immutability
Scala and functional programs
Null pointer uncertainty
Easier parallelism
Interoperability with Java
When not to use Scala
Summary
References
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3
2. Manipulating Data with Breeze
Code examples
Installing Breeze
Getting help on Breeze
Basic Breeze data types
Vectors
Dense and sparse vectors and the vector trait
Matrices
Building vectors and matrices
Advanced indexing and slicing
Mutating vectors and matrices
Matrix multiplication, transposition, and the orientation of vectors
Data preprocessing and feature engineering
Breeze – function optimization
Numerical derivatives
Regularization
An example – logistic regression
Towards re-usable code
Alternatives to Breeze
Summary
References
3. Plotting with breeze-viz
Diving into Breeze
Customizing plots
Customizing the line type
More advanced scatter plots
Multi-plot example – scatterplot matrix plots
Managing without documentation
Breeze-viz reference
Data visualization beyond breeze-viz
Summary
4. Parallel Collections and Futures
Parallel collections
Limitations of parallel collections
Error handling
Setting the parallelism level
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An example – cross-validation with parallel collections
Futures
Future composition – using a future's result
Blocking until completion
Controlling parallel execution with execution contexts
Futures example – stock price fetcher
Summary
References
5. Scala and SQL through JDBC
Interacting with JDBC
First steps with JDBC
Connecting to a database server
Creating tables
Inserting data
Reading data
JDBC summary
Functional wrappers for JDBC
Safer JDBC connections with the loan pattern
Enriching JDBC statements with the "pimp my library" pattern
Wrapping result sets in a stream
Looser coupling with type classes
Type classes
Coding against type classes
When to use type classes
Benefits of type classes
Creating a data access layer
Summary
References
6. Slick – A Functional Interface for SQL
FEC data
Importing Slick
Defining the schema
Connecting to the database
Creating tables
Inserting data
Querying data
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