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Añadir al carritohardcover. Condición: Good. 2nd Edition. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
Idioma: Inglés
Publicado por CRC Press | Taylor & Francis Group, Boca Raton, FL, 2015
ISBN 10: 1466583282 ISBN 13: 9781466583283
Librería: Twice Sold Tales, Capitol Hill, Seattle, WA, Estados Unidos de America
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Añadir al carritoHardcover, 437 pages. Condición: Very good. Light rubbing to extremities of glossy pictorial boards. Head of spine slightly bumped. Pages appear free of writing / highlighting. In nice shape.
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Idioma: Inglés
Publicado por Taylor & Francis Inc, Bosa Roca, 2014
ISBN 10: 1466583282 ISBN 13: 9781466583283
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Añadir al carritoHardcover. Condición: new. Hardcover. A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.New to the Second EditionTwo new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of contentRevision of the support vector machine material, including a simple implementation for experimentsNew material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptronAdditional discussions of the Kalman and particle filtersImproved code, including better use of naming conventions in PythonSuitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the authors website. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Añadir al carritoCondición: New. Num Pages: 457 pages, 205 black & white illustrations, 21 black & white tables. BIC Classification: UYQM. Category: (UU) Undergraduate. Dimension: 262 x 183 x 24. Weight in Grams: 1022. . 2014. 2nd Edition. Hardcover. . . . .
Idioma: Inglés
Publicado por Taylor and Francis Inc, US, 2014
ISBN 10: 1466583282 ISBN 13: 9781466583283
Librería: Rarewaves USA, OSWEGO, IL, Estados Unidos de America
EUR 130,21
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Añadir al carritoHardback. Condición: New. A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.New to the Second EditionTwo new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of contentRevision of the support vector machine material, including a simple implementation for experimentsNew material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptronAdditional discussions of the Kalman and particle filtersImproved code, including better use of naming conventions in PythonSuitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author's website.
Idioma: Inglés
Publicado por Taylor & Francis Group, 2014
ISBN 10: 1466583282 ISBN 13: 9781466583283
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Añadir al carritoCondición: very good. Le livre peut montrer des signes d'usure dus a une utilisation constante, etre marque, porter des marques d'identification ou presenter plusieurs dommages esthetiques mineurs. vendeur professionnel; envoi soigne en 24/48h.
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Añadir al carritoHardback. Condición: New. New copy - Usually dispatched within 4 working days.
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Añadir al carritoHardcover. Condición: Brand New. 2nd har/psc edition. 437 pages. 10.00x7.00x1.25 inches. In Stock.
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Idioma: Inglés
Publicado por Taylor and Francis Inc, US, 2014
ISBN 10: 1466583282 ISBN 13: 9781466583283
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
EUR 142,70
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Añadir al carritoHardback. Condición: New. A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.New to the Second EditionTwo new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of contentRevision of the support vector machine material, including a simple implementation for experimentsNew material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptronAdditional discussions of the Kalman and particle filtersImproved code, including better use of naming conventions in PythonSuitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author's website.
Idioma: Inglés
Publicado por Taylor & Francis Group, 2014
ISBN 10: 1466583282 ISBN 13: 9781466583283
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 128,62
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Añadir al carritoCondición: New. pp. 500.
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de America
EUR 140,33
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Añadir al carritoCondición: New. Num Pages: 457 pages, 205 black & white illustrations, 21 black & white tables. BIC Classification: UYQM. Category: (UU) Undergraduate. Dimension: 262 x 183 x 24. Weight in Grams: 1022. . 2014. 2nd Edition. Hardcover. . . . . Books ship from the US and Ireland.
Librería: moluna, Greven, Alemania
EUR 111,59
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Añadir al carritoCondición: New. Stephen Marsland is a professor of scientific computing and the postgraduate director of the School of Engineering and Advanced Technology (SEAT) at Massey University. His research interests in mathematical computing include shape spaces.
Idioma: Inglés
Publicado por Taylor and Francis Inc, US, 2014
ISBN 10: 1466583282 ISBN 13: 9781466583283
Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de America
EUR 133,15
Cantidad disponible: 2 disponibles
Añadir al carritoHardback. Condición: New. A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.New to the Second EditionTwo new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of contentRevision of the support vector machine material, including a simple implementation for experimentsNew material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptronAdditional discussions of the Kalman and particle filtersImproved code, including better use of naming conventions in PythonSuitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author's website.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 117,07
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Añadir al carritoBuch. Condición: Neu. Neuware - This bestseller helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. Along with improved Python code, this second edition includes two new chapters on deep belief networks and Gaussian processes. It incorporates new material on the support vector machine, random forests, the perceptron convergence theorem, filters, and more. All of the code is available on the author's website.
Librería: preigu, Osnabrück, Alemania
EUR 123,80
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Añadir al carritoBuch. Condición: Neu. Machine Learning | An Algorithmic Perspective, Second Edition | Stephen Marsland | Buch | Einband - fest (Hardcover) | Englisch | 2014 | CRC Press | EAN 9781466583283 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.