Deep Learning for Search
Applied book on using deep learning for search, including dense vector representations, semantic search, and neural ranking, all directly relevant to building applications on top of vector databases.
About this tool
Deep Learning for Search
Category: concepts-definitions
Type: Book / learning resource
Brand: Manning Publications
Source: https://www.manning.com/books/deep-learning-for-search
Overview
“Deep Learning for Search” is a practical book on applying deep learning to search systems. It focuses on dense vector representations, semantic search, and neural ranking, with concrete examples for building smarter search engines and applications on top of technologies like Lucene and modern DL frameworks.
Features
-
Neural search fundamentals
- Explains how deep learning relates to search basics such as indexing and ranking.
- Shows how to integrate neural networks into traditional search pipelines.
-
Improved ranking quality
- Techniques for achieving more accurate and relevant search result rankings.
- Methods to handle imprecise search terms and poorly indexed data.
-
Semantic and multilingual search
- Searching across languages using deep learning models.
- Translating user queries to improve cross-language retrieval.
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Dense vector and content-based search
- Use of dense vector representations for semantic similarity.
- Content-based image search using minimal metadata.
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Recommendation-enhanced search
- Integrating recommendation signals into search (e.g., “search with recommendations”).
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Practical implementations
- End-to-end examples using Apache Lucene.
- Deep learning implementations using Deeplearning4j (DL4J) and TensorFlow.
- Focus on using modern tools without requiring deep expertise in NLP or ML.
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Adaptive and learning search systems
- Designing search engines that improve over time as they learn from data.
Audience
- Developers comfortable with Java or a similar programming language.
- Readers familiar with basic search concepts (indexing, ranking, retrieval).
- No prior experience with deep learning or natural language processing (NLP) required.
Author
- Tommaso Teofili
- Software engineer focused on open source and machine learning.
- Member of the Apache Software Foundation; contributor to projects including:
- Information retrieval: Lucene, Solr
- NLP and machine translation: OpenNLP, Joshua, UIMA
- Works at Adobe on search and indexing infrastructure and related research.
- Conference speaker on search and machine learning (e.g., BerlinBuzzwords, ICCS, ApacheCon, EclipseCon).
Pricing
Manning subscription options shown for accessing this book (and possibly other content):
- Lite: $19.99 per month
- Pro: $24.99 per month
- Team: Plans for 5, 10, or 20 seats+ for teams (details via Manning’s corporate plans page).
Note: These are subscription prices as listed on the page, not necessarily the standalone book price. For exact and current pricing, see the source URL.
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