Graph Powered Machine Learning by Negro Alessandro (12 results)
- Softcover
Seller: Basi6 International, Irving, TX, U.S.A.Basi6 International
Contact seller5-star sellerCondition: New
£ 46.85
Free ShippingShips within U.S.A.Quantity: 5 available
Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
- Softcover
Seller: Rarewaves USA, HEBRON, KY, U.S.A.Rarewaves USA
Contact seller5-star sellerCondition: New
£ 47.46
Free ShippingShips within U.S.A.Quantity: 10 available
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
Seller: medimops, Berlin, Germanymedimops
Contact seller5-star sellerCondition: Used - Very good
£ 34.67
£ 17.11 shippingShips from Germany to U.S.A.Quantity: 1 available
Condition: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.
- Softcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
Contact seller5-star sellerCondition: New
£ 47.30
£ 5.87 shippingShips from United Kingdom to U.S.A.Quantity: 11 available
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
Contact seller5-star sellerCondition: New
£ 55.94
Free ShippingShips within U.S.A.Quantity: 1 available
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 Pearson,, 2021
- Softcover
Seller: Books in my Basket, New Delhi, IndiaBooks in my Basket
Contact seller5-star sellerSoft cover. Condition: New. ISBN:9781617295645.
- Softcover
Seller: Rarewaves USA United, HEBRON, KY, U.S.A.Rarewaves USA United
Contact seller5-star sellerCondition: New
£ 47.69
£ 36.94 shippingShips within U.S.A.Quantity: 10 available
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
Seller: UK BOOKS STORE, London, LONDO, United KingdomUK BOOKS STORE
Contact seller4-star sellerInternational EditionCondition: Used
£ 87.67
Free ShippingShips from United Kingdom to U.S.A.Quantity: 3 available
Paperback. Condition: New Books. 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 b…e requested 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.
- Softcover
Seller: Studibuch, Stuttgart, GermanyStudibuch
Contact seller5-star sellerCondition: Used - Fine
£ 34.27
£ 53.31 shippingShips from Germany to U.S.A.Quantity: 1 available
paperback. Condition: Sehr gut. 503 Seiten; 9781617295645.2 Gewicht in Gramm: 1.
- Softcover
Seller: moluna, Greven, Germanymoluna
Contact seller5-star sellerCondition: New
£ 50.20
£ 41.92 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: New. Über den AutorAlessandro 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.
- Softcover
Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contact seller5-star sellerCondition: New
£ 87.64
£ 27.34 shippingShips from Australia to U.S.A.Quantity: 1 available
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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Softcover
Seller: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, GermanyBUCHSERVICE / ANTIQUARIAT Lars Lutzer
Contact seller5-star sellerCondition: Used - Very good
£ 158.57
£ 34.19 shippingShips from Germany to U.S.A.Quantity: 1 available
Condition: gut. 2021. Graph-Powered Machine Learning In englischer Sprache. pages.
