Build a Reasoning Model (From Scratch)
Sebastian Raschka
Description A deep dive into the architecture and implementation of AI models capable of logical deduction and multi-step reasoning. It explains how t...
Deep Learning for Search
Tommaso Teofili
Book Metadata
Publisher
Simon and Schuster
Duration
8 hr 29 min
ISBN
9781638356271
Genres
Computers
About This Book
Description An exploration of neural network-based techniques to improve search relevance and effectiveness. The book discusses the integration of deep learning with traditional search infrastructures like Apache Lucene and Solr. Key Topics: Neural search, word and document embeddings, ranking algorithms, content-based image search, and cross-language retrieval. About the Technology: Deep learning allows search engines to process imprecise terms and non-textual data by representing content as vectors in a high-dimensional space. About the Book: The content reviews search basics like indexing and ranking before introducing deep learning models for query suggestion, document recommendation, and performance optimization. About the Reader: For developers familiar with Java and basic search concepts. About the Author: Tommaso Teofili is a software engineer and a member of the Apache Software Foundation, specializing in information retrieval and machine learning.
AI Summary
Deep Learning for Search shows Java‑focused developers how to enhance classic Lucene and Solr pipelines with neural models such as BERT, word2vec, and multimodal embeddings.
The book blends a brief IR refresher with hands‑on code and deployment tips for modern relevance tuning.
Technical Reference
Summary
Deep Learning for Search shows Java‑focused developers how to enhance classic Lucene and Solr pipelines with neural models such as BERT, word2vec, and multimodal embeddings. The book blends a brief IR refresher with hands‑on code and deployment tips for modern relevance tuning.
Key Themes
- Vector embeddings for text and images in search
- Combining neural models with Lucene/Solr architecture
- Neural reranking and ranking algorithm design
- Cross‑language and multimodal retrieval
- Performance, scaling, and production considerations
Ideal Reader
Java developers or software engineers familiar with basic search concepts who want to add deep‑learning relevance techniques to their skill set.
Details
stylepractical and example‑driven, with code snippets and configuration guidance
levelintermediate
commitmentreference plus deep dive suitable for both quick lookups and longer project work
Why It Matters
By focusing on real‑world Java search stacks, the book fills a scarce niche where traditional IR engineering meets neural relevance models, giving practitioners concrete steps to modernize legacy search services with state‑of‑the‑art deep learning techniques.
Related Reading
Elasticsearch: The Definitive Guide by Clinton Gormley & Zachary TongLearning Elasticsearch by Abhishek AndhavarapuNeural Search in Action by Michael Hunger et al.Information Retrieval: Implementing and Evaluating Search Engines by Bruce Croft
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