Deep Learning for Search

Name: Deep Learning for Search
Brand: Manning;Pearson Professional
Price: 46.84 EUR
Availability: OnlineOnly

Tommaso Teofili(Autor*in)

Manning;Pearson Professional (Verlag)

1. Auflage

Erschienen am 2. Juni 2019

328 Seiten

E-Book

ePUB mit Adobe-DRM

Systemvoraussetzungen

978-1-63835-627-1 (ISBN)

46,84 €inkl. 7% MwSt.

Systemvoraussetzungen

für ePUB mit Adobe-DRM

E-Book Einzellizenz

Als Download verfügbar

Beschreibung

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!

Foreword by Chris Mattmann.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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

Weitere Details

Weitere Ausgaben

Person

Inhalt

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
Summary
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
Summary
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
Summary
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
Summary
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
Summary
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
Summary
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
Summary
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
Summary
Looking forward
Index
List of Figures
List of Tables
List of Listings

Systemvoraussetzungen

Als PDF speichern Als Link merken