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Elasticsearch 8.x Cookbook

You're reading from   Elasticsearch 8.x Cookbook Over 180 recipes to perform fast, scalable, and reliable searches for your enterprise

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Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781801079815
Length 750 pages
Edition 5th Edition
Languages
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Author (1):
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Alberto Paro Alberto Paro
Author Profile Icon Alberto Paro
Alberto Paro
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Table of Contents (20) Chapters Close

Preface 1. Chapter 1: Getting Started 2. Chapter 2: Managing Mappings FREE CHAPTER 3. Chapter 3: Basic Operations 4. Chapter 4: Exploring Search Capabilities 5. Chapter 5: Text and Numeric Queries 6. Chapter 6: Relationships and Geo Queries 7. Chapter 7: Aggregations 8. Chapter 8: Scripting in Elasticsearch 9. Chapter 9: Managing Clusters 10. Chapter 10: Backups and Restoring Data 11. Chapter 11: User Interfaces 12. Chapter 12: Using the Ingest Module 13. Chapter 13: Java Integration 14. Chapter 14: Scala Integration 15. Chapter 15: Python Integration 16. Chapter 16: Plugin Development 17. Chapter 17: Big Data Integration 18. Chapter 18: X-Pack 19. Other Books You May Enjoy

Integrating with DeepLearning.scala

In the previous chapter, we learned how to use DeepLearning4j with Java. This library can be used natively in Scala to provide deep learning capabilities for our Scala applications.

In this recipe, we will learn how to use Elasticsearch as a source of training data in a machine learning algorithm.

Getting ready

You need an up-and-running Elasticsearch installation, as described in the Downloading and installing Elasticsearch recipe of Chapter 1, Getting Started.

Additionally, Maven, or an IDE that natively supports Java programming, such as Eclipse or IntelliJ IDEA, must be installed.

The code for this recipe can be found in the ch14/deeplearningscala directory.

We will use the iris dataset (https://en.wikipedia.org/wiki/Iris_flower_data_set) that we used in Chapter 13, Java Integration. To prepare your iris index dataset, we need to populate it by executing the PopulatingIndex class, which is available in the source code of Chapter...

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