Sprache: Englisch
Verlag: Cambridge University Press (edition 1), 2020
ISBN 10: 1108470041 ISBN 13: 9781108470049
Anbieter: BooksRun, Philadelphia, PA, USA
Hardcover. Zustand: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
Sprache: Englisch
Verlag: Cambridge University Press, 2020
ISBN 10: 1108470041 ISBN 13: 9781108470049
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 107,24
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Sprache: Englisch
Verlag: Cambridge University Press, 2021
ISBN 10: 1108470041 ISBN 13: 9781108470049
Anbieter: moluna, Greven, Deutschland
Zustand: 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, .
Sprache: Englisch
Verlag: Cambridge University Press, 2021
ISBN 10: 1108470041 ISBN 13: 9781108470049
Anbieter: preigu, Osnabrück, Deutschland
Buch. Zustand: Neu. Mathematics for Machine Learning | Marc Peter Deisenroth (u. a.) | Buch | Gebunden | Englisch | 2021 | Cambridge University Press | EAN 9781108470049 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Sprache: Englisch
Verlag: Cambridge University Press, 2020
ISBN 10: 1108470041 ISBN 13: 9781108470049
Anbieter: Kennys Bookstore, Olney, MD, USA
Zustand: New. 2020. Hardcover. . . . . . Books ship from the US and Ireland.
Sprache: Englisch
Verlag: Cambridge University Press Aug 2020, 2020
ISBN 10: 1108470041 ISBN 13: 9781108470049
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: 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.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 176,57
Anzahl: 2 verfügbar
In den WarenkorbHardcover. Zustand: Brand New. 398 pages. 10.00x7.00x1.00 inches. In Stock.