In Exploring Deep Learning for Search, author and deep learning guru Tommaso Teofili features three chapters from his book, Deep Learning for Search. Inside, you’ll see how neural search saves you time and improves search effectiveness by automating work that was previously done manually. You’ll also explore how to widen your search net by using a recurrent neural network (RNN) to add text-generation functionality to a search engine. In the final chapter, you’ll delve into using convolutional neural networks (CNNs) to index images and make them searchable by their content. With this laser-focused guide, you’ll have an excellent grasp on the basics of improving search with deep learning.
About the Technology
Giving users relevant search results is a challenge, especially when search terms are imprecise, data is poorly indexed, or images have minimal metadata. But neural networks and deep learning techniques can empower your search engines to return high quality results from even the sloppiest query! Even better, the more you use your search engines, the more they learn, providing better search results all the time!
Chapter 1, “Neural search”
Chapter 3, “From plain retrieval to text generation”
Chapter 8, “Content-based image search”
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.
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