Alessandro Negro (52 results)
Language: English
Published by American Geophysical Union, 2004
Series: Geophysical Monograph, Book 126 of 266. Book 126 of 266 - Geophysical Monograph
- Hardcover
- First Edition
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Hardcover. Condition: Good. 1st Edition. Very good condition. Ex-library.
Language: English
Published by Manning Publications, 2025
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
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Paperback. Condition: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
- Softcover
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Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Paperback. Condition: New. At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as…graphs. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You'll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you'll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls. Key Features · The lifecycle of a machine learning project · Three end-to-end applications · Graphs in big data platforms · Data source modeling · Natural language processing, recommendations, and relevant search · Optimization methods Readers comfortable with machine learning basics. About the technology By organizing and analyzing your data as graphs, your applications work more fluidly with graph-centric algorithms like nearest neighbor or page rank where it's important to quickly identify and exploit relevant relationships. Modern graph data stores, like Neo4j or Amazon Neptune, are readily available tools that support graph-powered machine learning. Alessandro Negro is a Chief Scientist at GraphAware. With extensive experience in software development, software architecture, and data management, he has been a speaker at many conferences, such as Java One, Oracle Open World, and Graph Connect. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning.
Knowledge Graphs and Llms in Action
Negro, Alessandro; Kus, Vlastimil; Futia, Giuseppe; Montagna, Fabio
Language: English
Published by Manning, 2025
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
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Language: English
Published by Manning Publications 11/18/2025, 2025
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
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Paperback or Softback. Condition: New. Knowledge Graphs and Llms in Action. Book.
Knowledge Graphs and Llms in Action
Negro, Alessandro; Kus, Vlastimil; Futia, Giuseppe; Montagna, Fabio
Language: English
Published by Manning, 2025
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
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Knowledge Graphs and LLMs in Action: Build AI systems using connected data
Negro, Alessandro; Kus, Vlastimil; Futia, Giuseppe; Montagna, Fabio
Language: English
Published by Manning, 2025
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
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Language: English
Published by Manning Publications, US, 2025
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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Paperback. Condition: New. Data overload, disconnected context, and stalled machine learning results are common frustrations for data teams. Even with vast datasets and advanced models, insights remain elusive when information is scattered and relationships are unclear. What if you could structure your data in a way that gives i…t meaning, connects the dots, and powers smarter, faster learning? By building knowledge graphs that integrate with large language models, you can transform disconnected information into actionable, context-rich intelligence that drives real results. Iterative top-down modeling: Aligns every graph decision with clear business questions. Ontology and taxonomy starters: Jump-start graph design from your existing structured data. Python code walk-throughs: Let you replicate techniques on day one, no guesswork. GNN and BERT integration: Upgrade graphs with deep learning for smarter reasoning and predictions. Real healthcare and policing cases: Prove scalability on messy, high-stakes datasets. Knowledge Graphs and LLMs in Action by GraphAware scientists Dr. Alessandro Negro and colleagues delivers a code-rich softcover reference that unites cutting-edge research with field-tested engineering practice. Starting with business questions, you model ontologies, import varied sources, then iteratively expand your graph. Later chapters layer GNNs, transformers, and reasoning algorithms, showing complete pipelines on full-scale datasets. You will leave with repeatable workflows, reusable code, and the confidence to connect fragmented data into intelligent, context-aware applications. Stop guessing; start delivering measurable machine learning impact.
- Softcover
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
Language: English
Published by Manning Publications, New York, 2025
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Paperback. Condition: new. Paperback. Data overload, disconnected context, and stalled machine learning results are common frustrations for data teams. Even with vast datasets and advanced models, insights remain elusive when information is scattered and relationships are unclear. What if you could structure your data in a way t…hat gives it meaning, connects the dots, and powers smarter, faster learning? By building knowledge graphs that integrate with large language models, you can transform disconnected information into actionable, context-rich intelligence that drives real results. Iterative top-down modeling: Aligns every graph decision with clear business questions. Ontology and taxonomy starters: Jump-start graph design from your existing structured data. Python code walk-throughs: Let you replicate techniques on day one, no guesswork. GNN and BERT integration: Upgrade graphs with deep learning for smarter reasoning and predictions. Real healthcare and policing cases: Prove scalability on messy, high-stakes datasets. Knowledge Graphs and LLMs in Action by GraphAware scientists Dr. Alessandro Negro and colleagues delivers a code-rich softcover reference that unites cutting-edge research with field-tested engineering practice. Starting with business questions, you model ontologies, import varied sources, then iteratively expand your graph. Later chapters layer GNNs, transformers, and reasoning algorithms, showing complete pipelines on full-scale datasets. You will leave with repeatable workflows, reusable code, and the confidence to connect fragmented data into intelligent, context-aware applications. Stop guessing; start delivering measurable machine learning impact. Knowledge graphs represent a real paradigm shift in the way that machines can understand data by effectively modeling the contextual information thats vital for human knowledge. Theyre poised to help revolutionize data analysis and machine learning, with applications ranging from search engines to e-commerce and more. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Softcover
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Paperback. Condition: new. Paperback. At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally ex…pressed as graphs. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. Youll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, youll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls. Key Features The lifecycle of a machine learning project Three end-to-end applications Graphs in big data platforms Data source modeling Natural language processing, recommendations, and relevant search Optimization methods Readers comfortable with machine learning basics. About the technology By organizing and analyzing your data as graphs, your applications work more fluidly with graph-centric algorithms like nearest neighbor or page rank where its important to quickly identify and exploit relevant relationships. Modern graph data stores, like Neo4j or Amazon Neptune, are readily available tools that support graph-powered machine learning. Alessandro Negro is a Chief Scientist at GraphAware. With extensive experience in software development, software architecture, and data management, he has been a speaker at many conferences, such as Java One, Oracle Open World, and Graph Connect. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Language: English
Published by American Geophysical Union, 2004
Series: Geophysical Monograph, Book 126 of 266. Book 126 of 266 - Geophysical Monograph
- Hardcover
Seller: ThriftBooks-Dallas, Dallas, TX, U.S.A.ThriftBooks-Dallas
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Hardcover. Condition: As New. No Jacket. Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, Spend Less.
- Softcover
Seller: WorldofBooks, Goring-By-Sea, WS, United KingdomWorldofBooks
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Paperback. Condition: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Mt. Etna: Volcano Laboratory (Geophysical Monograph Series)
Bonaccorso, Alessandro [Editor]; Calvari, Sonia [Editor]; Coltelli, Mauro [Editor]; Del Negro, Ciro [Editor]; Falsaperla, Susanna [Editor];
Language: English
Published by American Geophysical Union, 2004
Series: Geophysical Monograph, Book 126 of 266. Book 126 of 266 - Geophysical Monograph
- Hardcover
Seller: JIM1024, WEST DES MOINES, IA, U.S.A.JIM1024
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hardcover. Condition: Very Good. Library withdrawn- interior looks nearly like new- CD-ROM is included. SCI-4.
Language: English
Published by Manning Publications, US, 2025
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
Contact seller5-star sellerCondition: New
£ 59.15
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Paperback. Condition: New. Data overload, disconnected context, and stalled machine learning results are common frustrations for data teams. Even with vast datasets and advanced models, insights remain elusive when information is scattered and relationships are unclear. What if you could structure your data in a way that gives i…t meaning, connects the dots, and powers smarter, faster learning? By building knowledge graphs that integrate with large language models, you can transform disconnected information into actionable, context-rich intelligence that drives real results. Iterative top-down modeling: Aligns every graph decision with clear business questions. Ontology and taxonomy starters: Jump-start graph design from your existing structured data. Python code walk-throughs: Let you replicate techniques on day one, no guesswork. GNN and BERT integration: Upgrade graphs with deep learning for smarter reasoning and predictions. Real healthcare and policing cases: Prove scalability on messy, high-stakes datasets. Knowledge Graphs and LLMs in Action by GraphAware scientists Dr. Alessandro Negro and colleagues delivers a code-rich softcover reference that unites cutting-edge research with field-tested engineering practice. Starting with business questions, you model ontologies, import varied sources, then iteratively expand your graph. Later chapters layer GNNs, transformers, and reasoning algorithms, showing complete pipelines on full-scale datasets. You will leave with repeatable workflows, reusable code, and the confidence to connect fragmented data into intelligent, context-aware applications. Stop guessing; start delivering measurable machine learning impact.
Knowledge Graphs and Llms in Action
Negro, Alessandro; Kus, Vlastimil; Futia, Giuseppe; Montagna, Fabio
Language: English
Published by Manning, 2025
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
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Knowledge Graphs and Llms in Action
Negro, Alessandro; Kus, Vlastimil; Futia, Giuseppe; Montagna, Fabio
Language: English
Published by Manning, 2025
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
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Condition: As New. Unread book in perfect condition.
Language: English
Published by Manning Publications, 2025
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
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Paperback / softback. Condition: New. New copy - Usually dispatched within 2 working days.
Language: English
Published by Pearson,, 2021
- Softcover
Seller: Books in my Basket, New Delhi, IndiaBooks in my Basket
Contact seller5-star sellerSoft cover. Condition: New. ISBN:9781617295645.
Published by Accademia Nazionale dei Lincei, Roma, 1974
- Softcover
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Contact seller4-star sellerOpuscolo in 8°, pp. 50. Con 16 figure nel testo. Brossura editoriale. Memorie. Classe di Scienze fisiche, matematiche e naturali. Serie VIII, Vol. XII, Fasc. 1. Copia in stato di nuovo, a fogli chiusi.
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Language: English
Published by Manning, 2025
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
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paperback. Condition: New. Special order direct from the distributor.
Language: English
Published by Manning Publications, 2026
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
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Condition: New. 2026. 1st Edition. paperback. . . . . .
Knowledge Graphs and Llms in Action
Negro, Alessandro/ Kus, Vlastimil/ Futia, Giuseppe/ Montagna, Fabio
Language: English
Published by Manning Pubns Co, 2025
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
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Knowledge Graphs and Llms in Action
Negro, Alessandro/ Kus, Vlastimil/ Futia, Giuseppe/ Montagna, Fabio
Language: English
Published by Manning Pubns Co, 2025
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 425 pages. 9.26x7.38x9.25 inches. In Stock.
Language: English
Published by Manning Publications, 2025
Series: In Action, Book 159 of 182. Book 159 of 182 - In Action
- Softcover
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Condition: New. 2026. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.
- Hardcover
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Hardback. Condition: New.
- Softcover
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Paperback. Condition: New. At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as…graphs. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You'll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you'll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls. Key Features · The lifecycle of a machine learning project · Three end-to-end applications · Graphs in big data platforms · Data source modeling · Natural language processing, recommendations, and relevant search · Optimization methods Readers comfortable with machine learning basics. About the technology By organizing and analyzing your data as graphs, your applications work more fluidly with graph-centric algorithms like nearest neighbor or page rank where it's important to quickly identify and exploit relevant relationships. Modern graph data stores, like Neo4j or Amazon Neptune, are readily available tools that support graph-powered machine learning. Alessandro Negro is a Chief Scientist at GraphAware. With extensive experience in software development, software architecture, and data management, he has been a speaker at many conferences, such as Java One, Oracle Open World, and Graph Connect. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning.
- Softcover
- International Edition
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Paperback. Condition: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requ…ested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
