Neural Search, from Prototype to Production with Jina Build Deep Learning-Powered Search Systems That You Can Deploy and Manage with Ease

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Birmingham : Packt Publishing, Limited, 2022.
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Available Online

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Format
Edition
1st edition.
Language
English
ISBN
9781801818803, 1801818800

Notes

General Note
Description based upon print version of record.
Description
Implement neural search systems on the cloud by leveraging Jina design patterns Key Features Identify the different search techniques and discover applications of neural search Gain a solid understanding of vector representation and apply your knowledge in neural search Unlock deeper levels of knowledge of Jina for neural search Book Description Search is a big and ever-growing part of the tech ecosystem. Traditional search, however, has limitations that are hard to overcome because of the way it is designed. Neural search is a novel approach that uses the power of machine learning to retrieve information using vector embeddings as first-class citizens, opening up new possibilities of improving the results obtained through traditional search. Although neural search is a powerful tool, it is new and finetuning it can be tedious as it requires you to understand the several components on which it relies. Jina fills this gap by providing an infrastructure that reduces the time and complexity involved in creating deep learning-powered search engines. This book will enable you to learn the fundamentals of neural networks for neural search, its strengths and weaknesses, as well as how to use Jina to build a search engine. With the help of step-by-step explanations, practical examples, and self-assessment questions, you'll become well-versed with the basics of neural search and core Jina concepts, and learn to apply this knowledge to build your own search engine. By the end of this deep learning book, you'll be able to make the most of Jina's neural search design patterns to build an end-to-end search solution for any modality. What you will learn Understand how neural search and legacy search work Grasp the machine learning and math fundamentals needed for neural search Get to grips with the foundation of vector representation Explore the basic components of Jina Analyze search systems with different modalities Uncover the capabilities of Jina with the help of practical examples Who this book is for If you are a machine learning, deep learning, or artificial intelligence engineer interested in building a search system of any kind (text, QA, image, audio, PDF, 3D models, or others) using modern software architecture, this book is for you. This book is perfect for Python engineers who are interested in building a search system of any kind using state-of-the-art deep learning techniques.
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Citations

APA Citation, 7th Edition (style guide)

Wang, B. (. i. s., Mitroi, C., Wang, F., Saboo, S., & Guzman, S. (2022). Neural Search, from Prototype to Production with Jina: Build Deep Learning-Powered Search Systems That You Can Deploy and Manage with Ease (1st edition.). Packt Publishing, Limited.

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

Bo (Artificial intelligence scientist), Wang et al.. 2022. Neural Search, From Prototype to Production With Jina: Build Deep Learning-Powered Search Systems That You Can Deploy and Manage With Ease. Birmingham: Packt Publishing, Limited.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

Bo (Artificial intelligence scientist), Wang et al.. Neural Search, From Prototype to Production With Jina: Build Deep Learning-Powered Search Systems That You Can Deploy and Manage With Ease Birmingham: Packt Publishing, Limited, 2022.

Harvard Citation (style guide)

Wang, B. (. i. s., Mitroi, C., Wang, F., Saboo, S. and Guzman, S. (2022). Neural search, from prototype to production with jina: build deep learning-powered search systems that you can deploy and manage with ease. 1st edn. Birmingham: Packt Publishing, Limited.

MLA Citation, 9th Edition (style guide)

Wang, Bo (Artificial intelligence scientist),, et al. Neural Search, From Prototype to Production With Jina: Build Deep Learning-Powered Search Systems That You Can Deploy and Manage With Ease 1st edition., Packt Publishing, Limited, 2022.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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4dad5e7e-d5fc-bba9-c9ac-e59a25b1bd58-eng
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Grouped Work ID4dad5e7e-d5fc-bba9-c9ac-e59a25b1bd58-eng
Full titleneural search from prototype to production with jina build deep learning powered search systems that you can deploy and manage with ease
Authorwang bo
Grouping Categorybook
Last Update2025-02-20 07:23:03AM
Last Indexed2025-05-03 03:13:05AM

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5050 |a Table of Contents Neural Networks for Neural Search Introducing Foundations of Vector Representation System Design and Engineering Challenges Learning Jina's Basics Multiple Search Modalities Basic Practical Examples with Jina Exploring Advanced Use Cases of Jina.
520 |a Implement neural search systems on the cloud by leveraging Jina design patterns Key Features Identify the different search techniques and discover applications of neural search Gain a solid understanding of vector representation and apply your knowledge in neural search Unlock deeper levels of knowledge of Jina for neural search Book Description Search is a big and ever-growing part of the tech ecosystem. Traditional search, however, has limitations that are hard to overcome because of the way it is designed. Neural search is a novel approach that uses the power of machine learning to retrieve information using vector embeddings as first-class citizens, opening up new possibilities of improving the results obtained through traditional search. Although neural search is a powerful tool, it is new and finetuning it can be tedious as it requires you to understand the several components on which it relies. Jina fills this gap by providing an infrastructure that reduces the time and complexity involved in creating deep learning-powered search engines. This book will enable you to learn the fundamentals of neural networks for neural search, its strengths and weaknesses, as well as how to use Jina to build a search engine. With the help of step-by-step explanations, practical examples, and self-assessment questions, you'll become well-versed with the basics of neural search and core Jina concepts, and learn to apply this knowledge to build your own search engine. By the end of this deep learning book, you'll be able to make the most of Jina's neural search design patterns to build an end-to-end search solution for any modality. What you will learn Understand how neural search and legacy search work Grasp the machine learning and math fundamentals needed for neural search Get to grips with the foundation of vector representation Explore the basic components of Jina Analyze search systems with different modalities Uncover the capabilities of Jina with the help of practical examples Who this book is for If you are a machine learning, deep learning, or artificial intelligence engineer interested in building a search system of any kind (text, QA, image, audio, PDF, 3D models, or others) using modern software architecture, this book is for you. This book is perfect for Python engineers who are interested in building a search system of any kind using state-of-the-art deep learning techniques.
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