Deep learning for search / Tommaso Teofili ; foreword by Chris Mattmann.

Format:

Book

Author/Creator:

Teofili, Tommaso, author.

Contributor:

Mattmann, Chris, writer of foreword.

Language:

English

Subjects (All):

Machine learning.

Web search engines.

Neural networks (Computer science).

Web sites--Design.

Web sites.

Physical Description:

1 online resource (328 pages)

Edition:

1st edition

Place of Publication:

Shelter Island : Manning, [2019]

System Details:

text file

Summary:

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!

Contents:

Intro

Copyright

Brief Table of Contents

Table of Contents

Foreword

Preface

Acknowledgments

About this book

About the author

About the cover illustration

Part 1. Search meets deep learning

Chapter 1. Neural search

1.1. Neural networks and deep learning

1.2. What is machine learning?

1.3. What deep learning can do for search

1.4. A roadmap for learning deep learning

1.5. Retrieving useful information

1.6. Unsolved problems

1.7. Opening the search engine black box

1.8. Deep learning to the rescue

1.9. Index, please meet neuron

1.10. Neural network training

1.11. The promises of neural search

Summary

Chapter 2. Generating synonyms

2.1. Introduction to synonym expansion

2.2. The importance of context

2.3. Feed-forward neural networks

2.4. Using word2vec

2.5. Evaluations and comparisons

2.6. Considerations for production systems

Part 2. Throwing neural nets at a search engine

Chapter 3. From plain retrieval to text generation

3.1. Information need vs. query: Bridging the gap

3.2. Learning over sequences

3.3. Recurrent neural networks

3.4. LSTM networks for unsupervised text generation

3.5. From unsupervised to supervised text generation

3.6. Considerations for production systems

Chapter 4. More-sensitive query suggestions

4.1. Generating query suggestions

4.2. Lucene Lookup APIs

4.3. Analyzed suggesters

4.4. Using language models

4.5. Content-based suggesters

4.6. Neural language models

4.7. Character-based neural language model for suggestions

4.8. Tuning the LSTM language model

4.9. Diversifying suggestions using word embeddings

Chapter 5. Ranking search results with word embeddings

5.1. The importance of ranking

5.2. Retrieval models.

5.3. Neural information retrieval

5.4. From word to document vectors

5.5. Evaluations and comparisons

Chapter 6. Document embeddings for rankings and recommendations

6.1. From word to document embeddings

6.2. Using paragraph vectors in ranking

6.3. Document embeddings and related content

Part 3. One step beyond

Chapter 7. Searching across languages

7.1. Serving users who speak multiple languages

7.2. Statistical machine translation

7.3. Working with parallel corpora

7.4. Neural machine translation

7.5. Word and document embeddings for multiple languages

Chapter 8. Content-based image search

8.1. Image contents and search

8.2. A look back: Text-based image retrieval

8.3. Understanding images

8.4. Deep learning for image representation

8.5. Working with unlabeled images

Chapter 9. A peek at performance

9.1. Performance and the promises of deep learning

9.2. Indexes and neurons working together

9.3. Working with streams of data

Looking forward

Index

List of Figures

List of Tables

List of Listings.

Notes:

Description based on print version record.

Includes index.

ISBN:

9781638356271

1638356270

OCLC:

1122594715