Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 110845514X ISBN 13: 9781108455145
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 110845514X ISBN 13: 9781108455145
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Cambridge University Press (edition 1), 2020
ISBN 10: 1108470041 ISBN 13: 9781108470049
Da: BooksRun, Philadelphia, PA, U.S.A.
Hardcover. Condizione: Fair. 1. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2020
ISBN 10: 110845514X ISBN 13: 9781108455145
Da: Rarewaves.com USA, London, LONDO, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: New. The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2020
ISBN 10: 110845514X ISBN 13: 9781108455145
Da: Rarewaves USA, OSWEGO, IL, U.S.A.
EUR 66,90
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Aggiungi al carrelloPaperback. Condizione: New. The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108470041 ISBN 13: 9781108470049
Da: Goodwill of Silicon Valley, SAN JOSE, CA, U.S.A.
Condizione: good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Good condition! Any other included accessories are also in Good condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear.
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 110845514X ISBN 13: 9781108455145
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 51,67
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 60,55
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Aggiungi al carrelloCondizione: New. In.
Da: Chiron Media, Wallingford, Regno Unito
EUR 57,18
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 74,78
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Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 110845514X ISBN 13: 9781108455145
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 59,37
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 60,13
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Aggiungi al carrelloCondizione: New.
Condizione: New. pp. 440.
ISBN 10: 1009108859 ISBN 13: 9781009108850
Da: Basi6 International, Irving, TX, U.S.A.
Condizione: Brand New. New.SoftCover International edition. Different ISBN and Cover image but contents are same as US edition.Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
Da: Revaluation Books, Exeter, Regno Unito
EUR 83,12
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Aggiungi al carrelloPaperback. Condizione: Brand New. 398 pages. 9.25x6.25x0.25 inches. In Stock.
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 110845514X ISBN 13: 9781108455145
Da: moluna, Greven, Germania
EUR 48,97
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Aggiungi al carrelloCondizione: New. This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, .
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108470041 ISBN 13: 9781108470049
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 103,69
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2020
ISBN 10: 110845514X ISBN 13: 9781108455145
Da: Rarewaves USA United, OSWEGO, IL, U.S.A.
EUR 68,68
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Aggiungi al carrelloPaperback. Condizione: New. The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing, 2015
ISBN 10: 3319233432 ISBN 13: 9783319233437
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 53,49
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the proceedings of the International Conference on Brain Informatics and Health, BIH 2015, held in London, UK, in August/September 2015. The 42 full papers presented were carefully reviewed and selected from 82 submissions. Following the success of past conferences in this series, BIH 2015 has a strong emphasis on emerging trends of big data analysis and management technology for brain research, behavior learning, and real-world applications of brain science in human health and wellbeing.
Lingua: Inglese
Editore: Cambridge University Press 2020-04-23, 2020
ISBN 10: 1108470041 ISBN 13: 9781108470049
Da: Chiron Media, Wallingford, Regno Unito
EUR 104,01
Quantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: New.
Lingua: Inglese
Editore: Springer-Verlag New York Inc, 2015
ISBN 10: 3319233432 ISBN 13: 9783319233437
Da: Revaluation Books, Exeter, Regno Unito
EUR 81,36
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Aggiungi al carrelloPaperback. Condizione: Brand New. 476 pages. 9.25x6.10x1.08 inches. In Stock.
Da: preigu, Osnabrück, Germania
EUR 50,35
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Brain Informatics and Health | 8th International Conference, BIH 2015, London, UK, August 30 - September 2, 2015. Proceedings | Yike Guo (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xv | Englisch | 2015 | Springer | EAN 9783319233437 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108470041 ISBN 13: 9781108470049
Da: Ria Christie Collections, Uxbridge, Regno Unito
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Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108470041 ISBN 13: 9781108470049
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 123,27
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Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108470041 ISBN 13: 9781108470049
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 106,96
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Lingua: Inglese
Editore: Cambridge University Press, 2021
ISBN 10: 1108470041 ISBN 13: 9781108470049
Da: moluna, Greven, Germania
EUR 80,46
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Aggiungi al carrelloCondizione: New. This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, .
Lingua: Inglese
Editore: Cambridge University Press, GB, 2020
ISBN 10: 110845514X ISBN 13: 9781108455145
Da: Rarewaves.com UK, London, Regno Unito
EUR 60,67
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Aggiungi al carrelloPaperback. Condizione: New. The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108470041 ISBN 13: 9781108470049
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 122,06
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2020
ISBN 10: 1108470041 ISBN 13: 9781108470049
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 144,12
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Aggiungi al carrelloHardback. Condizione: New. The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2020
ISBN 10: 1108470041 ISBN 13: 9781108470049
Da: Rarewaves USA, OSWEGO, IL, U.S.A.
EUR 150,05
Quantità: Più di 20 disponibili
Aggiungi al carrelloHardback. Condizione: New. The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.