Librería: St Vincent de Paul of Lane County, Eugene, OR, Estados Unidos de America
EUR 12,92
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Añadir al carritoCondición: Good. paperback 100% of proceeds go to charity! Good condition with all pages in tact. Item shows signs of use and may have cosmetic defects.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 14,52
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Librería: Lakeside Books, Benton Harbor, MI, Estados Unidos de America
EUR 13,31
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Añadir al carritoCondición: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books!
Idioma: Inglés
Publicado por O'Reilly Media 10/6/2014, 2014
ISBN 10: 1491915382 ISBN 13: 9781491915387
Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de America
EUR 17,34
Cantidad disponible: 5 disponibles
Añadir al carritoPaperback or Softback. Condición: New. Practical Machine Learning: Innovations in Recommendation. Book.
Librería: Rarewaves USA, OSWEGO, IL, Estados Unidos de America
EUR 17,73
Cantidad disponible: Más de 20 disponibles
Añadir al carritoPaperback. Condición: New. Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settings--and demonstrates how even a small-scale development team can design an effective large-scale recommendation system.Apache Mahout committers Ted Dunning and Ellen Friedman walk you through a design that relies on careful simplification. You'll learn how to collect the right data, analyze it with an algorithm from the Mahout library, and then easily deploy the recommender using search technology, such as Apache Solr or Elasticsearch. Powerful and effective, this efficient combination does learning offline and delivers rapid response recommendations in real time.Understand the tradeoffs between simple and complex recommendersCollect user data that tracks user actions--rather than their ratingsPredict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysisUse search technology to offer recommendations in real time, complete with item metadataWatch the recommender in action with a music service exampleImprove your recommender with dithering, multimodal recommendation, and other techniques.
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
EUR 17,94
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Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 16,10
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Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
EUR 17,65
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Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
EUR 21,77
Cantidad disponible: 2 disponibles
Añadir al carritoPaperback. Condición: New. Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settings--and demonstrates how even a small-scale development team can design an effective large-scale recommendation system.Apache Mahout committers Ted Dunning and Ellen Friedman walk you through a design that relies on careful simplification. You'll learn how to collect the right data, analyze it with an algorithm from the Mahout library, and then easily deploy the recommender using search technology, such as Apache Solr or Elasticsearch. Powerful and effective, this efficient combination does learning offline and delivers rapid response recommendations in real time.Understand the tradeoffs between simple and complex recommendersCollect user data that tracks user actions--rather than their ratingsPredict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysisUse search technology to offer recommendations in real time, complete with item metadataWatch the recommender in action with a music service exampleImprove your recommender with dithering, multimodal recommendation, and other techniques.
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 19,70
Cantidad disponible: 3 disponibles
Añadir al carritoCondición: new.
Idioma: Inglés
Publicado por Oreilly & Associates Inc, 2014
ISBN 10: 1491915382 ISBN 13: 9781491915387
Librería: Revaluation Books, Exeter, Reino Unido
EUR 20,88
Cantidad disponible: 2 disponibles
Añadir al carritoPaperback. Condición: Brand New. 48 pages. 8.75x6.00x0.25 inches. In Stock.
Idioma: Inglés
Publicado por O'Reilly Media, Inc, USA, 2014
ISBN 10: 1491915382 ISBN 13: 9781491915387
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 20,16
Cantidad disponible: 3 disponibles
Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 31,31
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Añadir al carritoCondición: New. pp. 56.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 17,64
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 19,72
Cantidad disponible: 3 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 27,33
Cantidad disponible: 1 disponibles
Añadir al carritoCondición: New. 2016. Paperback. . . . . .
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de America
EUR 33,59
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Añadir al carritoCondición: New. 2016. Paperback. . . . . . Books ship from the US and Ireland.
Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de America
EUR 19,24
Cantidad disponible: Más de 20 disponibles
Añadir al carritoPaperback. Condición: New. Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settings--and demonstrates how even a small-scale development team can design an effective large-scale recommendation system.Apache Mahout committers Ted Dunning and Ellen Friedman walk you through a design that relies on careful simplification. You'll learn how to collect the right data, analyze it with an algorithm from the Mahout library, and then easily deploy the recommender using search technology, such as Apache Solr or Elasticsearch. Powerful and effective, this efficient combination does learning offline and delivers rapid response recommendations in real time.Understand the tradeoffs between simple and complex recommendersCollect user data that tracks user actions--rather than their ratingsPredict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysisUse search technology to offer recommendations in real time, complete with item metadataWatch the recommender in action with a music service exampleImprove your recommender with dithering, multimodal recommendation, and other techniques.
EUR 19,63
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Añadir al carritoCondición: New. Über den AutorrnrnTed Dunning is Chief Applications Architect at MapR Technologies and committer and PMC member of the Apache Mahout, ZooKeeper, and Drill projects and mentor for the Apache Storm, DataFu, Flink, and Optiq projects. .
Idioma: Inglés
Publicado por O'reilly Media Nov 2014, 2014
ISBN 10: 1491915382 ISBN 13: 9781491915387
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 22,98
Cantidad disponible: 2 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. Neuware - Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settingsand demonstrates how even a small-scale development team can design an effective large-scale recommendation system.
Librería: Rarewaves.com UK, London, Reino Unido
EUR 19,21
Cantidad disponible: 2 disponibles
Añadir al carritoPaperback. Condición: New. Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settings--and demonstrates how even a small-scale development team can design an effective large-scale recommendation system.Apache Mahout committers Ted Dunning and Ellen Friedman walk you through a design that relies on careful simplification. You'll learn how to collect the right data, analyze it with an algorithm from the Mahout library, and then easily deploy the recommender using search technology, such as Apache Solr or Elasticsearch. Powerful and effective, this efficient combination does learning offline and delivers rapid response recommendations in real time.Understand the tradeoffs between simple and complex recommendersCollect user data that tracks user actions--rather than their ratingsPredict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysisUse search technology to offer recommendations in real time, complete with item metadataWatch the recommender in action with a music service exampleImprove your recommender with dithering, multimodal recommendation, and other techniques.