AI-Powered Search (MEAP v15)

Автор: literator от 26-01-2023, 22:09, Коментариев: 0


AI-Powered Search (MEAP v15)Название: AI-Powered Search (MEAP V15)
Автор: Trey Grainger, Doug Turnbull, Max Irwin
Издательство: Manning Publications
Год: 2023
Страниц: 583
Язык: английский
Формат: epub
Размер: 48.1 MB

AI-Powered Search teaches you the latest Machine Learning techniques to create search engines that continuously learn from your users and your content, to drive more domain-aware and intelligent search.

Today’s search engines are expected to be smart, understanding the nuances of natural language queries, as well as each user’s preferences and context. AI-Powered Search is an authoritative guide to applying leading-edge Data Science techniques to search.

As you can imagine given that goal, this is not an “introduction to search” book. In order to get the most out of this book, you should ideally already be familiar with the core capabilities of modern search engines (inverted indices, relevance ranking, faceting, query parsing, text analysis, and so on) through experience with a technology like Apache Solr, Elasticsearch/OpenSearch, Vespa, or Apache Lucene. If you need to come up to speed quickly, Solr in Action (which I also wrote) provides you with all the search background necessary to dive head-first into AI-Powered Search.

Additionally, the code examples in this book are written in Python (and delivered in pre-configured Jupyter notebooks) to appeal both to engineers and data scientists. You don’t need to be an expert in Python, but you should have some programming experience to be able to read and understand the examples.

Written by Trey Grainger, the Chief Algorithms Officer at Lucidworks, AI-Powered Search empowers you to create and deploy search engines that take advantage of user interactions. Working through code in interactive notebooks, you'll learn how to build search engines that automatically understand the intention of a query in order to deliver significantly better results.

This book is an example-driven guide through the most applicable machine learning algorithms and techniques commonly leveraged to build intelligent search systems. We’ll not only walk through key concepts, but will also provide reusable code examples to cover data collection and processing techniques, as well as the self-learning query interpretation and relevance strategies employed to deliver AI-powered search capabilities across today’s leading organizations - hopefully soon to include your own!

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