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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

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783558742
Length 624 pages
Edition 1st Edition
Languages
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Author (1):
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 R. Nicolas R. Nicolas
Author Profile Icon R. Nicolas
R. Nicolas
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Table of Contents (20) Chapters Close

Scala for Machine Learning
Credits
About the Author
About the Reviewers
www.PacktPub.com
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 Basic Concepts Index

A workflow computational model


Monads are very useful for manipulating and chaining data transformations using implicit configurations or explicit models. However, they are restricted to a single morphism T => U type. More complex and flexible workflows require weaving transformations of different types using a generic factory pattern.

Traditional factory patterns rely on a combination of composition and inheritance and do not provide developers with the same level of flexibility as stackable traits.

In this section, we introduce you to the concept of modeling using mixins and a variant of the cake pattern to provide a workflow with three degrees of configurability.

Supporting mathematical abstractions

Stackable traits enable developers to follow a strict mathematical formalism while implementing a model in Scala. Scientists use a universally accepted template to solve a mathematical problem:

  1. Declare the variables relevant to the problem.

  2. Define a model (equations, algorithms, formulas, and...

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