Paperback. Condizione: As New. It's a preowned item in almost perfect condition. It has no visible cosmetic imperfections. May come without any shrink wrap; pages are clean and not marred by notes or folds of any kind.
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Condizione: very_good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in very good condition! The cover and any other included accessories are also in very good condition showing some minor use. The spine is straight, there are no rips tears or creases on the cover or the pages.
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Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Editore: Manning Publications, New York, 2023
ISBN 10: 1617297135 ISBN 13: 9781617297137
Lingua: Inglese
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condizione: new. Paperback. Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. About the Technology Ensemble machine learning lets you make robust predictions without needing the huge datasets and processing power demanded by deep learning. It sets multiple models to work on solving a problem, combining their results for better performance than a single model working alone. This "wisdom of crowds" approach distils information from several models into a set of highly accurate results. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Editore: Manning Publications 2023-06-09, 2023
ISBN 10: 1617297135 ISBN 13: 9781617297137
Lingua: Inglese
Da: Chiron Media, Wallingford, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 350 pages. 9.25x7.37x0.88 inches. In Stock.
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Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 3 working days.
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Paperback. Condizione: New. Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. About the Technology Ensemble machine learning lets you make robust predictions without needing the huge datasets and processing power demanded by deep learning. It sets multiple models to work on solving a problem, combining their results for better performance than a single model working alone. This "wisdom of crowds" approach distils information from several models into a set of highly accurate results.
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Prima edizione
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Aggiungi al carrelloCondizione: New. 2023. 1st Edition. Paperback. . . . . .
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 56,70
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real-world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. 352 pp. Englisch.
Da: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real-world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. 352 pp. Englisch.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real-world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models.
Condizione: New. 2023. 1st Edition. Paperback. . . . . . Books ship from the US and Ireland.
Editore: Manning Publications, New York, 2023
ISBN 10: 1617297135 ISBN 13: 9781617297137
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. About the Technology Ensemble machine learning lets you make robust predictions without needing the huge datasets and processing power demanded by deep learning. It sets multiple models to work on solving a problem, combining their results for better performance than a single model working alone. This "wisdom of crowds" approach distils information from several models into a set of highly accurate results. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Aggiungi al carrelloCondizione: NEW.
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Aggiungi al carrelloCondizione: New. Über den AutorrnrnGautam Kunapuli has over 15 years of experience in academia and the machine learning industry. He has developed several novel algorithms for diverse application domains including social network analysis, text and natu.
EUR 91,52
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Aggiungi al carrelloPaperback. Condizione: Brand New. 350 pages. 9.25x7.37x0.88 inches. In Stock.
EUR 56,70
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real-world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models.Manning, St.-Martin-Straße 82, 81541 München 352 pp. Englisch.