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SummaryDeep 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 TechnologyDeep 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 BookDeep 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 insideAccurate and relevant rankingsSearching across languagesContent-based image searchSearch with recommendationsAbout the ReaderFor developers comfortable with Java or a similar language and search basics. No experience with deep learning or NLP needed.About the AuthorTommaso 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 ContentsPART 1 - SEARCH MEETS DEEP LEARNINGNeural searchGenerating synonymsPART 2 - THROWING NEURAL NETS AT A SEARCH ENGINEFrom plain retrieval to text generationMore-sensitive query suggestionsRanking search results with word embeddingsDocument embeddings for rankings and recommendationsPART 3 - ONE STEP BEYONDSearching across languagesContent-based image searchA peek at performance
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SummaryDeep 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 TechnologyDeep 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 BookDeep 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 insideAccurate and relevant rankingsSearching across languagesContent-based image searchSearch with recommendationsAbout the ReaderFor developers comfortable with Java or a similar language and search basics. No experience with deep learning or NLP needed.About the AuthorTommaso 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 ContentsPART 1 - SEARCH MEETS DEEP LEARNINGNeural searchGenerating synonymsPART 2 - THROWING NEURAL NETS AT A SEARCH ENGINEFrom plain retrieval to text generationMore-sensitive query suggestionsRanking search results with word embeddingsDocument embeddings for rankings and recommendationsPART 3 - ONE STEP BEYONDSearching across languagesContent-based image searchA peek at performance
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