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Deep Learning for Search

Summary

Deep Learning for Search teaches you how to improve the effectiveness of your search by implementing neural network-based techniques. By the time you're finished with the book, you'll be ready to build amazing search engines that deliver the results your users need and that get better as time goes on!

About the Technology

Deep learning handles the toughest search challenges, including imprecise search terms, badly indexed data, and retrieving images with minimal metadata. And with modern tools like DL4J and TensorFlow, you can apply powerful DL techniques without a deep background in data science or natural language processing (NLP). This book will show you how.

About the Book

Deep Learning for Search teaches you to improve your search results with neural networks. You'll review how DL relates to search basics like indexing and ranking. Then, you'll walk through in-depth examples to upgrade your search with DL techniques using Apache Lucene and Deeplearning4j. As the book progresses, you'll explore advanced topics like searching through images, translating user queries, and designing search engines that improve as they learn!

What's inside
  • Accurate and relevant rankings
  • Searching across languages
  • Content-based image search
  • Search with recommendations
About the Reader

For developers comfortable with Java or a similar language and search basics. No experience with deep learning or NLP needed.

About the Author

Tommaso Teofili is a software engineer with a passion for open source and machine learning. As a member of the Apache Software Foundation, he contributes to a number of open source projects, ranging from topics like information retrieval (such as Lucene and Solr) to natural language processing and machine translation (including OpenNLP, Joshua, and UIMA).

He currently works at Adobe, developing search and indexing infrastructure components, and researching the areas of natural language processing, information retrieval, and deep learning. He has presented search and machine learning talks at conferences including BerlinBuzzwords, International Conference on Computational Science, ApacheCon, EclipseCon, and others. You can find him on Twitter at @tteofili.

Table of Contents

PART 1 - SEARCH MEETS DEEP LEARNING
  • Neural search
  • Generating synonyms
PART 2 - THROWING NEURAL NETS AT A SEARCH ENGINE
  • From plain retrieval to text generation
  • More-sensitive query suggestions
  • Ranking search results with word embeddings
  • Document embeddings for rankings and recommendations
PART 3 - ONE STEP BEYOND
  • Searching across languages
  • Content-based image search
  • A peek at performance

You will get a PDF (8MB) file

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