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Scala for Machine Learning, Second Edition

You're reading from Scala for Machine Learning, Second Edition Build systems for data processing, machine learning, and deep learning

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781787122383

Length 740 pages

Edition 2nd Edition

Languages

Scala

Tools

Apache Spark

Concepts

Data Processing

Author (1):

Patrick R. Nicolas

View More author details

Table of Contents (21) Chapters

Preface

1. Getting Started FREE CHAPTER

2. Data Pipelines

3. Data Preprocessing

4. Unsupervised Learning

5. Dimension Reduction

6. Naïve Bayes Classifiers

7. Sequential Data Models

8. Monte Carlo Inference

9. Regression and Regularization

10. Multilayer Perceptron

11. Deep Learning

12. Kernel Models and SVM

13. Evolutionary Computing

14. Multiarmed Bandits

15. Reinforcement Learning

16. Parallelism in Scala and Akka

17. Apache Spark MLlib

A. Basic Concepts

B. References

Index

Chapter 2. Data Pipelines

In the first chapter, you were acquainted with some rudimentary concepts regarding data processing, clustering, and classification.

This chapter is dedicated to the creation and maintenance of a flexible end-to-end workflow to train and classify data. The first section of the chapter introduces a data-centric (functional) approach to create number crunching applications, followed by a description of a configurable workflow computation model. The chapter concludes with an overview of different model validation techniques.

You will learn how to do the following:

Apply the concept of monadic design to create dynamic workflows
Leverage some of Scala's advanced patterns, such as the cake pattern, to build portable computational workflows
Take into account the bias-variance trade-off in selecting a model
Overcome overfitting in modeling
Break down data into training, test and validation sets
Implement model validation in Scala using precision, recall, and F score...

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Authors (1)

R. Nicolas

R. Nicolas

Patrick R. Nicolas is the director of engineering at Agile SDE, California. He has more than 25 years of experience in software engineering and building applications in C++, Java, and more recently in Scala/Spark, and has held several managerial positions. His interests include real-time analytics, modeling, and the development of nonlinear models.

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