Ong cheng soon (38 risultati)
Lingua: Inglese
Editore: Cambridge University Press, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
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Da: ThriftBooks-Atlanta, AUSTELL, GA, U.S.A.ThriftBooks-Atlanta
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EUR 39,38
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Paperback. Condizione: As New. No Jacket. Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, Spend Less.
Lingua: Inglese
Editore: Cambridge University Press, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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EUR 52,47
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Condizione: New.
Lingua: Inglese
Editore: Cambridge University Press, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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EUR 56,14
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Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Cambridge University Press (edition 1), 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
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Da: BooksRun, Philadelphia, PA, U.S.A.BooksRun
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EUR 62,40
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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
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Brossura
Da: Rarewaves.com USA, London, LONDO, Regno UnitoRarewaves.com USA
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 66,01
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Paperback. 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
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Brossura
Da: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 66,99
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
Paperback. 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
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Brossura
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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EUR 52,09
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Condizione: New.
Lingua: Inglese
Editore: Cambridge University Press, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Brossura
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Come nuovo
EUR 55,29
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Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Cambridge University Pr. Apr 2020, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Brossura
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 52,30
EUR 23,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. Neuware -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 o…r 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. Englisch.
Lingua: Inglese
Editore: Cambridge University Pr. Apr 2020, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Brossura
Da: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, GermaniaRheinberg-Buch Andreas Meier eK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 52,30
EUR 23,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. Neuware -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 o…r 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. Englisch.
Lingua: Inglese
Editore: Cambridge University Pr. Apr 2020, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Brossura
Da: Wegmann1855, Zwiesel, GermaniaWegmann1855
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 53,00
EUR 25,95 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. Neuware -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 o…r 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 studentsand otherswith a mathematical background, these derivations provide a starting point to machine learning texts. Forthoselearning 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.
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- Edizione Internazionale
Da: Basi6 International, Irving, TX, U.S.A.Basi6 International
Contatta il venditoreVenditore con 5 stelleEdizione InternazionaleCondizione: Nuovo
EUR 41,63
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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.
Lingua: Inglese
Editore: Cambridge Univ Pr, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Brossura
Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 83,84
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Paperback. Condizione: Brand New. 398 pages. 9.25x6.25x0.25 inches. In Stock.
Lingua: Inglese
Editore: Cambridge University Press, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Brossura
Da: moluna, Greven, Germaniamoluna
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EUR 49,40
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Condizione: 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
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Rilegato
Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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EUR 104,02
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Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Brossura
Da: Rarewaves USA United, OSWEGO, IL, U.S.A.Rarewaves USA United
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 68,79
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Paperback. 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 Pr. Apr 2020, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Brossura
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 53,00
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. Neuware -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 o…r 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 studentsand otherswith a mathematical background, these derivations provide a starting point to machine learning texts. Forthoselearning 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.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld Englisch.
Lingua: Inglese
Editore: Cambridge University Pr. Apr 2020, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 52,30
EUR 64,05 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. Neuware - 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 2020-04-23, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Rilegato
Da: Chiron Media, Wallingford, Regno UnitoChiron Media
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EUR 104,87
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Hardcover. Condizione: New.
Lingua: Inglese
Editore: Cambridge University Press, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Rilegato
Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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EUR 110,29
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Condizione: New. In.
Lingua: Inglese
Editore: Cambridge University Press, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Condizione: New.
Lingua: Inglese
Editore: Cambridge University Press, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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EUR 124,25
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Condizione: New.
Lingua: Inglese
Editore: Cambridge University Press CUP, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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EUR 127,48
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Condizione: New.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Brossura
Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
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EUR 61,17
EUR 75,85 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
Paperback. 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
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Rilegato
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Come nuovo
EUR 123,15
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Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Rilegato
Da: Rarewaves.com USA, London, LONDO, Regno UnitoRarewaves.com USA
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 145,31
Spedizione gratuitaSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
Hardback. 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 s…cience 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
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Rilegato
Da: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
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EUR 150,25
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Hardback. 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 s…cience 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, 2021
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Rilegato
Da: moluna, Greven, Germaniamoluna
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 114,13
EUR 48,99 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Condizione: 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
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Rilegato
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 117,81
EUR 64,63 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 cours…es, 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 Univ Pr, 2020
Serie: Libro 34 di 38 - Studies in Natural Language Processing
- Rilegato
Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 178,15
EUR 14,59 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 2 disponibili
Hardcover. Condizione: Brand New. 398 pages. 10.00x7.00x1.00 inches. In Stock.
