Neural Search, from Prototype to Production with Jina: Build Deep Learning-Powered Search Systems That You Can Deploy and Manage with Ease
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 knowl...
Gespeichert in:
| Beteiligte Personen: | , , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham
Packt Publishing, Limited
2022
|
| Ausgabe: | 1st edition. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781801816823/?ar |
| Zusammenfassung: | 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. |
| Beschreibung: | Description based upon print version of record |
| Umfang: | 1 Online-Ressource (188 Seiten) |
| ISBN: | 9781801818803 1801818800 |
Internformat
MARC
| LEADER | 00000cam a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-082535795 | ||
| 003 | DE-627-1 | ||
| 005 | 20240228121815.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 221111s2022 xx |||||o 00| ||eng c | ||
| 020 | |a 9781801818803 |9 978-1-80181-880-3 | ||
| 020 | |a 1801818800 |9 1-80181-880-0 | ||
| 035 | |a (DE-627-1)082535795 | ||
| 035 | |a (DE-599)KEP082535795 | ||
| 035 | |a (ORHE)9781801816823 | ||
| 035 | |a (DE-627-1)082535795 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 082 | 0 | |a 006.3/2 |2 23/eng/20221018 | |
| 100 | 1 | |a Wang, Bo |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Neural Search, from Prototype to Production with Jina |b Build Deep Learning-Powered Search Systems That You Can Deploy and Manage with Ease |c Bo Wang, Cristian Mitroi, Feng Wang, Shubam Saboo, Susana Guzmán |
| 250 | |a 1st edition. | ||
| 264 | 1 | |a Birmingham |b Packt Publishing, Limited |c 2022 | |
| 300 | |a 1 Online-Ressource (188 Seiten) | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Description based upon print version of record | ||
| 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. | ||
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 0 | |a Search engines |x Technological innovations | |
| 650 | 0 | |a Deep learning (Machine learning) | |
| 650 | 4 | |a Réseaux neuronaux (Informatique) | |
| 650 | 4 | |a Moteurs de recherche ; Innovations | |
| 650 | 4 | |a Apprentissage profond | |
| 650 | 4 | |a Deep learning (Machine learning) | |
| 650 | 4 | |a Neural networks (Computer science) | |
| 700 | 1 | |a Mitroi, Cristian |e VerfasserIn |4 aut | |
| 700 | 1 | |a Wang, Feng |e VerfasserIn |4 aut | |
| 700 | 1 | |a Saboo, Shubham |e VerfasserIn |4 aut | |
| 700 | 1 | |a Guzman, Susana |e VerfasserIn |4 aut | |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781801816823/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 912 | |a ZDB-30-ORH | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Datensatz im Suchindex
| DE-BY-TUM_katkey | ZDB-30-ORH-082535795 |
|---|---|
| _version_ | 1821494818270871552 |
| adam_text | |
| any_adam_object | |
| author | Wang, Bo Mitroi, Cristian Wang, Feng Saboo, Shubham Guzman, Susana |
| author_facet | Wang, Bo Mitroi, Cristian Wang, Feng Saboo, Shubham Guzman, Susana |
| author_role | aut aut aut aut aut |
| author_sort | Wang, Bo |
| author_variant | b w bw c m cm f w fw s s ss s g sg |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)082535795 (DE-599)KEP082535795 (ORHE)9781801816823 |
| dewey-full | 006.3/2 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/2 |
| dewey-search | 006.3/2 |
| dewey-sort | 16.3 12 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | 1st edition. |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04330cam a22005052 4500</leader><controlfield tag="001">ZDB-30-ORH-082535795</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228121815.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221111s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781801818803</subfield><subfield code="9">978-1-80181-880-3</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1801818800</subfield><subfield code="9">1-80181-880-0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)082535795</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP082535795</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781801816823</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)082535795</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3/2</subfield><subfield code="2">23/eng/20221018</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Bo</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Neural Search, from Prototype to Production with Jina</subfield><subfield code="b">Build Deep Learning-Powered Search Systems That You Can Deploy and Manage with Ease</subfield><subfield code="c">Bo Wang, Cristian Mitroi, Feng Wang, Shubam Saboo, Susana Guzmán</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st edition.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham</subfield><subfield code="b">Packt Publishing, Limited</subfield><subfield code="c">2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (188 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based upon print version of record</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Neural networks (Computer science)</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Search engines</subfield><subfield code="x">Technological innovations</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Deep learning (Machine learning)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Réseaux neuronaux (Informatique)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Moteurs de recherche ; Innovations</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Apprentissage profond</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning (Machine learning)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural networks (Computer science)</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mitroi, Cristian</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Feng</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Saboo, Shubham</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guzman, Susana</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/9781801816823/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-30-ORH-082535795 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-17T11:20:25Z |
| institution | BVB |
| isbn | 9781801818803 1801818800 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (188 Seiten) |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Packt Publishing, Limited |
| record_format | marc |
| spelling | Wang, Bo VerfasserIn aut Neural Search, from Prototype to Production with Jina Build Deep Learning-Powered Search Systems That You Can Deploy and Manage with Ease Bo Wang, Cristian Mitroi, Feng Wang, Shubam Saboo, Susana Guzmán 1st edition. Birmingham Packt Publishing, Limited 2022 1 Online-Ressource (188 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Description based upon print version of record 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. Neural networks (Computer science) Search engines Technological innovations Deep learning (Machine learning) Réseaux neuronaux (Informatique) Moteurs de recherche ; Innovations Apprentissage profond Mitroi, Cristian VerfasserIn aut Wang, Feng VerfasserIn aut Saboo, Shubham VerfasserIn aut Guzman, Susana VerfasserIn aut |
| spellingShingle | Wang, Bo Mitroi, Cristian Wang, Feng Saboo, Shubham Guzman, Susana Neural Search, from Prototype to Production with Jina Build Deep Learning-Powered Search Systems That You Can Deploy and Manage with Ease Neural networks (Computer science) Search engines Technological innovations Deep learning (Machine learning) Réseaux neuronaux (Informatique) Moteurs de recherche ; Innovations Apprentissage profond |
| title | Neural Search, from Prototype to Production with Jina Build Deep Learning-Powered Search Systems That You Can Deploy and Manage with Ease |
| title_auth | Neural Search, from Prototype to Production with Jina Build Deep Learning-Powered Search Systems That You Can Deploy and Manage with Ease |
| title_exact_search | Neural Search, from Prototype to Production with Jina Build Deep Learning-Powered Search Systems That You Can Deploy and Manage with Ease |
| title_full | Neural Search, from Prototype to Production with Jina Build Deep Learning-Powered Search Systems That You Can Deploy and Manage with Ease Bo Wang, Cristian Mitroi, Feng Wang, Shubam Saboo, Susana Guzmán |
| title_fullStr | Neural Search, from Prototype to Production with Jina Build Deep Learning-Powered Search Systems That You Can Deploy and Manage with Ease Bo Wang, Cristian Mitroi, Feng Wang, Shubam Saboo, Susana Guzmán |
| title_full_unstemmed | Neural Search, from Prototype to Production with Jina Build Deep Learning-Powered Search Systems That You Can Deploy and Manage with Ease Bo Wang, Cristian Mitroi, Feng Wang, Shubam Saboo, Susana Guzmán |
| title_short | Neural Search, from Prototype to Production with Jina |
| title_sort | neural search from prototype to production with jina build deep learning powered search systems that you can deploy and manage with ease |
| title_sub | Build Deep Learning-Powered Search Systems That You Can Deploy and Manage with Ease |
| topic | Neural networks (Computer science) Search engines Technological innovations Deep learning (Machine learning) Réseaux neuronaux (Informatique) Moteurs de recherche ; Innovations Apprentissage profond |
| topic_facet | Neural networks (Computer science) Search engines Technological innovations Deep learning (Machine learning) Réseaux neuronaux (Informatique) Moteurs de recherche ; Innovations Apprentissage profond |
| work_keys_str_mv | AT wangbo neuralsearchfromprototypetoproductionwithjinabuilddeeplearningpoweredsearchsystemsthatyoucandeployandmanagewithease AT mitroicristian neuralsearchfromprototypetoproductionwithjinabuilddeeplearningpoweredsearchsystemsthatyoucandeployandmanagewithease AT wangfeng neuralsearchfromprototypetoproductionwithjinabuilddeeplearningpoweredsearchsystemsthatyoucandeployandmanagewithease AT sabooshubham neuralsearchfromprototypetoproductionwithjinabuilddeeplearningpoweredsearchsystemsthatyoucandeployandmanagewithease AT guzmansusana neuralsearchfromprototypetoproductionwithjinabuilddeeplearningpoweredsearchsystemsthatyoucandeployandmanagewithease |