Graph-powered machine learning:
I learned so much from this unique and comprehensive book. A real gem for anyone who wants to explore graph-powered ML apps. Helen Mary Labao-Barrameda, Okada Manila Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. In Graph-Pow...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Weitere beteiligte Personen: | |
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
[Shelter Island, New York]
Manning Publications
2021
|
| Ausgabe: | [First edition]. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781617295645AU/?ar https://learning.oreilly.com/library/view/~/9781617295645AU/?ar |
| Zusammenfassung: | I learned so much from this unique and comprehensive book. A real gem for anyone who wants to explore graph-powered ML apps. Helen Mary Labao-Barrameda, Okada Manila Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. In Graph-Powered Machine Learning you will learn: The lifecycle of a machine learning project Graphs in big data platforms Data source modeling using graphs Graph-based natural language processing, recommendations, and fraud detection techniques Graph algorithms Working with Neo4J Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You'll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro's extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! about the technology Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems. about the book Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you'll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks. about the audience For readers comfortable with machine learning basics. about the author Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science. The single best source of information for graph-based machine learning. Odysseas Pentakalos, SYSNET International, Inc I learned a lot. Plenty of 'aha!' moments. Jose San Leandro Armendáriz, OSOCO.es Covers all of the bases and enough real-world examples for you to apply the techniques to your own work. Richard Vaughan, Purple Monkey Collective NARRATED BY JULIE BRIERLEY. |
| Beschreibung: | Online resource; title from title details screen (O'Reilly, viewed May 4, 2022) |
| Umfang: | 1 Online-Ressource (1 audio file (12 hr., 26 min.)) |
Internformat
MARC
| LEADER | 00000cam a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-078670594 | ||
| 003 | DE-627-1 | ||
| 005 | 20240228121656.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220609s2021 xx |||||o 00| ||eng c | ||
| 035 | |a (DE-627-1)078670594 | ||
| 035 | |a (DE-599)KEP078670594 | ||
| 035 | |a (ORHE)9781617295645AU | ||
| 035 | |a (DE-627-1)078670594 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 082 | 0 | |a 006.31 |2 23 | |
| 100 | 1 | |a Negro, Alessandro |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Graph-powered machine learning |c Alessandro Negro |
| 250 | |a [First edition]. | ||
| 264 | 1 | |a [Shelter Island, New York] |b Manning Publications |c 2021 | |
| 300 | |a 1 Online-Ressource (1 audio file (12 hr., 26 min.)) | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Online resource; title from title details screen (O'Reilly, viewed May 4, 2022) | ||
| 520 | |a I learned so much from this unique and comprehensive book. A real gem for anyone who wants to explore graph-powered ML apps. Helen Mary Labao-Barrameda, Okada Manila Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. In Graph-Powered Machine Learning you will learn: The lifecycle of a machine learning project Graphs in big data platforms Data source modeling using graphs Graph-based natural language processing, recommendations, and fraud detection techniques Graph algorithms Working with Neo4J Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You'll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro's extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! about the technology Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems. about the book Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you'll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks. about the audience For readers comfortable with machine learning basics. about the author Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science. The single best source of information for graph-based machine learning. Odysseas Pentakalos, SYSNET International, Inc I learned a lot. Plenty of 'aha!' moments. Jose San Leandro Armendáriz, OSOCO.es Covers all of the bases and enough real-world examples for you to apply the techniques to your own work. Richard Vaughan, Purple Monkey Collective NARRATED BY JULIE BRIERLEY. | ||
| 650 | 0 | |a Machine learning | |
| 650 | 0 | |a Graph theory | |
| 650 | 0 | |a Machine learning |x Graphic methods | |
| 650 | 4 | |a Apprentissage automatique | |
| 650 | 4 | |a Apprentissage automatique ; Méthodes graphiques | |
| 650 | 4 | |a Graph theory | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Machine learning ; Graphic methods | |
| 650 | 4 | |a Downloadable audio books | |
| 650 | 4 | |a Audiobooks | |
| 650 | 4 | |a Audiobooks | |
| 650 | 4 | |a Livres audio | |
| 700 | 1 | |a Brierley, Julie |e ErzählerIn |4 nrt | |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781617295645AU/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/~/9781617295645AU/?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-078670594 |
|---|---|
| _version_ | 1821494821059035136 |
| adam_text | |
| any_adam_object | |
| author | Negro, Alessandro |
| author2 | Brierley, Julie |
| author2_role | nrt |
| author2_variant | j b jb |
| author_facet | Negro, Alessandro Brierley, Julie |
| author_role | aut |
| author_sort | Negro, Alessandro |
| author_variant | a n an |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)078670594 (DE-599)KEP078670594 (ORHE)9781617295645AU |
| dewey-full | 006.31 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.31 |
| dewey-search | 006.31 |
| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | [First edition]. |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04435cam a22005052 4500</leader><controlfield tag="001">ZDB-30-ORH-078670594</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228121656.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220609s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)078670594</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP078670594</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781617295645AU</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)078670594</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">006.31</subfield><subfield code="2">23</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Negro, Alessandro</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Graph-powered machine learning</subfield><subfield code="c">Alessandro Negro</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">[First edition].</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">[Shelter Island, New York]</subfield><subfield code="b">Manning Publications</subfield><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (1 audio file (12 hr., 26 min.))</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">Online resource; title from title details screen (O'Reilly, viewed May 4, 2022)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">I learned so much from this unique and comprehensive book. A real gem for anyone who wants to explore graph-powered ML apps. Helen Mary Labao-Barrameda, Okada Manila Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. In Graph-Powered Machine Learning you will learn: The lifecycle of a machine learning project Graphs in big data platforms Data source modeling using graphs Graph-based natural language processing, recommendations, and fraud detection techniques Graph algorithms Working with Neo4J Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You'll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro's extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! about the technology Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems. about the book Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you'll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks. about the audience For readers comfortable with machine learning basics. about the author Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science. The single best source of information for graph-based machine learning. Odysseas Pentakalos, SYSNET International, Inc I learned a lot. Plenty of 'aha!' moments. Jose San Leandro Armendáriz, OSOCO.es Covers all of the bases and enough real-world examples for you to apply the techniques to your own work. Richard Vaughan, Purple Monkey Collective NARRATED BY JULIE BRIERLEY.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Graph theory</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning</subfield><subfield code="x">Graphic methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Apprentissage automatique</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Apprentissage automatique ; Méthodes graphiques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph theory</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning ; Graphic methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Downloadable audio books</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Audiobooks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Audiobooks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Livres audio</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Brierley, Julie</subfield><subfield code="e">ErzählerIn</subfield><subfield code="4">nrt</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/-/9781617295645AU/?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="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/~/9781617295645AU/?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-078670594 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-17T11:20:28Z |
| institution | BVB |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (1 audio file (12 hr., 26 min.)) |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Manning Publications |
| record_format | marc |
| spelling | Negro, Alessandro VerfasserIn aut Graph-powered machine learning Alessandro Negro [First edition]. [Shelter Island, New York] Manning Publications 2021 1 Online-Ressource (1 audio file (12 hr., 26 min.)) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online resource; title from title details screen (O'Reilly, viewed May 4, 2022) I learned so much from this unique and comprehensive book. A real gem for anyone who wants to explore graph-powered ML apps. Helen Mary Labao-Barrameda, Okada Manila Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. In Graph-Powered Machine Learning you will learn: The lifecycle of a machine learning project Graphs in big data platforms Data source modeling using graphs Graph-based natural language processing, recommendations, and fraud detection techniques Graph algorithms Working with Neo4J Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You'll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro's extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! about the technology Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems. about the book Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you'll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks. about the audience For readers comfortable with machine learning basics. about the author Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science. The single best source of information for graph-based machine learning. Odysseas Pentakalos, SYSNET International, Inc I learned a lot. Plenty of 'aha!' moments. Jose San Leandro Armendáriz, OSOCO.es Covers all of the bases and enough real-world examples for you to apply the techniques to your own work. Richard Vaughan, Purple Monkey Collective NARRATED BY JULIE BRIERLEY. Machine learning Graph theory Machine learning Graphic methods Apprentissage automatique Apprentissage automatique ; Méthodes graphiques Machine learning ; Graphic methods Downloadable audio books Audiobooks Livres audio Brierley, Julie ErzählerIn nrt |
| spellingShingle | Negro, Alessandro Graph-powered machine learning Machine learning Graph theory Machine learning Graphic methods Apprentissage automatique Apprentissage automatique ; Méthodes graphiques Machine learning ; Graphic methods Downloadable audio books Audiobooks Livres audio |
| title | Graph-powered machine learning |
| title_auth | Graph-powered machine learning |
| title_exact_search | Graph-powered machine learning |
| title_full | Graph-powered machine learning Alessandro Negro |
| title_fullStr | Graph-powered machine learning Alessandro Negro |
| title_full_unstemmed | Graph-powered machine learning Alessandro Negro |
| title_short | Graph-powered machine learning |
| title_sort | graph powered machine learning |
| topic | Machine learning Graph theory Machine learning Graphic methods Apprentissage automatique Apprentissage automatique ; Méthodes graphiques Machine learning ; Graphic methods Downloadable audio books Audiobooks Livres audio |
| topic_facet | Machine learning Graph theory Machine learning Graphic methods Apprentissage automatique Apprentissage automatique ; Méthodes graphiques Machine learning ; Graphic methods Downloadable audio books Audiobooks Livres audio |
| work_keys_str_mv | AT negroalessandro graphpoweredmachinelearning AT brierleyjulie graphpoweredmachinelearning |