Deep Learning for SearchSimon and Schuster, 2 июн. 2019 г. - Всего страниц: 328 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
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
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Содержание
Generating synonyms | |
Throwing neural nets at a search engine | |
Ranking search results with word embeddings | |
Summary | |
One step beyond | |
Contentbased image search | |
A peek at performance | |
Index | |
List of Figures | |
List of Tables | |
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accuracy aeroplane algorithm alternative queries Analyzer Apache Lucene artificial intelligence autoencoder backpropagation bernhard bernhard bernhard bernhard riemann build chapter character CIFAR ClassicSimilarity contains Creates dataset decoder deep learning deep neural networks dictionary DL4J document embeddings document vectors encoder encoder-decoder evaluation example extract F1 Score feature vectors feed-forward Figure filter hidden layer implement INDArray IndexReader IndexSearcher information retrieval input layer input query inverted index Iteration label language model Ledgewood Circle Listing look lookup Lucene machine learning machine translation match matrix neural language model neural search neurons ngram output layer paragraph vectors parameters perform probability query expansion query parser ranking function related content relevant retrieval models ScoreDoc search engine search results semantics sentence sequence similar skip-gram String suggestions synonym expansion techniques text analysis TF-IDF thought vector token unsupervised learning weights Wikipedia word embeddings word vectors word2vec model