Publicado por Cambridge University Press, 2019
ISBN 10: 1107057132 ISBN 13: 9781107057135
Idioma: Inglés
Librería: Buchpark, Trebbin, Alemania
EUR 43,40
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Añadir al carritoCondición: Gut. Zustand: Gut | Seiten: 414 | Sprache: Englisch | Produktart: Bücher.
Publicado por Cambridge University Press CUP, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Idioma: Inglés
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 72,58
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Añadir al carritoCondición: New. pp. 424.
Publicado por Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Idioma: Inglés
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 65,85
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Publicado por Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Idioma: Inglés
Librería: Majestic Books, Hounslow, Reino Unido
EUR 73,49
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Añadir al carritoCondición: New. pp. 424 47 Illus.
Publicado por Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Idioma: Inglés
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 67,39
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Publicado por Cambridge University Press, GB, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Idioma: Inglés
Librería: Rarewaves.com UK, London, Reino Unido
EUR 87,70
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Añadir al carritoHardback. Condición: New. Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
Publicado por Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Idioma: Inglés
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 74,94
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Añadir al carritoCondición: As New. Unread book in perfect condition.
Publicado por Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Idioma: Inglés
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 76,25
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Añadir al carritoCondición: As New. Unread book in perfect condition.
Publicado por Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Idioma: Inglés
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 77,04
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Añadir al carritoCondición: New. pp. 424.
Publicado por Cambridge University Press, GB, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Idioma: Inglés
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
EUR 93,77
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Añadir al carritoHardback. Condición: New. Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
Publicado por Cambridge University Press, GB, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Idioma: Inglés
Librería: Rarewaves USA, OSWEGO, IL, Estados Unidos de America
EUR 93,10
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Añadir al carritoHardback. Condición: New. Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
Publicado por Cambridge University Press, 2016
ISBN 10: 1107057132 ISBN 13: 9781107057135
Idioma: Inglés
Librería: moluna, Greven, Alemania
EUR 73,66
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Añadir al carritoGebunden. Condición: New. Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the hows an.
Publicado por Cambridge University Press, GB, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Idioma: Inglés
Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de America
EUR 95,71
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Añadir al carritoHardback. Condición: New. Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 100,66
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Añadir al carritoHardcover. Condición: Brand New. 397 pages. 10.00x7.00x1.00 inches. In Stock.
Publicado por Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Idioma: Inglés
Librería: HPB-Red, Dallas, TX, Estados Unidos de America
EUR 35,46
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Añadir al carritoHardcover. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Librería: Revaluation Books, Exeter, Reino Unido
EUR 69,78
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Añadir al carritoHardcover. Condición: Brand New. 397 pages. 10.00x7.00x1.00 inches. In Stock. This item is printed on demand.