Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
Da: Books From California, Simi Valley, CA, U.S.A.
hardcover. Condizione: Very Good. Cover and edges may have some wear.
Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
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hardcover. Condizione: New.
Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
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Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
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Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
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Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
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hardcover. Condizione: New. New. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
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Aggiungi al carrelloHardcover. Condizione: Like New. First Edition. Hardback book in nearly new condition: firm and square with strong joints. Just a few hardly noticeable rubs or very mild bumps. Hence a non-text page shows a small 'damaged' stamp. Despite such this book looks and feels unread. Thus the contents are crisp, fresh and tight. And so a very nice book in great condition, now offered for sale at a reasonable price.
Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition.
HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Lingua: Inglese
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Lingua: Inglese
Editore: Cambridge University Press CUP, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
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Condizione: Used. pp. 350 New edition niversity Press.
Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
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Lingua: Inglese
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ISBN 10: 1108843603 ISBN 13: 9781108843607
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Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
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Aggiungi al carrelloCondizione: Used. pp. 350.
Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
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ISBN 10: 1108843603 ISBN 13: 9781108843607
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Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
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Aggiungi al carrelloCondizione: New. 2022. New. Hardcover. . . . . .
Lingua: Inglese
Editore: Cambridge University Pr. Mär 2022, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 68,50
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware -This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning. 338 pp. Englisch.
Lingua: Inglese
Editore: Cambridge University Pr. Mär 2022, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
Da: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, Germania
EUR 68,50
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware -This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning. 338 pp. Englisch.
Lingua: Inglese
Editore: Cambridge University Pr. Mär 2022, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
Da: Wegmann1855, Zwiesel, Germania
EUR 68,50
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware -This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 102,02
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Aggiungi al carrelloHardback. Condizione: New. This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.
Lingua: Inglese
Editore: Cambridge University Press, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
Da: Kennys Bookstore, Olney, MD, U.S.A.
Condizione: New. 2022. New. Hardcover. . . . . . Books ship from the US and Ireland.
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Aggiungi al carrelloCondizione: New. This coherent introduction to machine learning for readers with a background in basic linear algebra, statistics, probability, and programming is suitable for advanced BSc or MSc courses. It covers theory and practice of basic and advanced methods such as d.
Da: Revaluation Books, Exeter, Regno Unito
EUR 102,28
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Aggiungi al carrelloHardcover. Condizione: Brand New. 325 pages. 10.20x7.20x0.80 inches. In Stock.
Lingua: Inglese
Editore: Cambridge University Pr. Mär 2022, 2022
ISBN 10: 1108843603 ISBN 13: 9781108843607
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 68,50
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware -This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 338 pp. Englisch.
EUR 63,60
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Aggiungi al carrelloBuch. Condizione: Neu. Machine Learning | A First Course for Engineers and Scientists | Andreas Lindholm (u. a.) | Buch | Gebunden | Englisch | 2022 | Cambridge University Pr. | EAN 9781108843607 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.