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.
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!