Editore: Cambridge University Press, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
Lingua: Inglese
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Editore: Cambridge University Press, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
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Editore: Cambridge University Press 4/5/2022, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
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Editore: Cambridge University Press, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
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Editore: Cambridge University Press, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
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Editore: Cambridge University Press, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
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Editore: Cambridge University Press, Cambridge, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundations of the analysis of nonsmooth functions and optimization duality; and the back-propagation approach, relevant to neural networks. Optimization techniques are at the core of data science. An understanding of the basic techniques and their fundamental properties provides important grounding for students, researchers, and practitioners. This compact, self-contained text covers the fundamentals of optimization algorithms, focusing on the techniques most relevant to data science. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Editore: Cambridge University Press, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
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Da: Majestic Books, Hounslow, Regno Unito
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Editore: Cambridge University Press, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
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Aggiungi al carrelloHardback. Condizione: New. New copy - Usually dispatched within 4 working days. 450.
Editore: Cambridge University Press, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
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Editore: Cambridge University Press, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
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ISBN 10: 1316518981 ISBN 13: 9781316518984
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ISBN 10: 1316518981 ISBN 13: 9781316518984
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Editore: Cambridge University Press 2021-10-31, 2021
ISBN 10: 1316518981 ISBN 13: 9781316518984
Lingua: Inglese
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Aggiungi al carrelloHardcover. Condizione: New.
Editore: Cambridge University Press, GB, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
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Aggiungi al carrelloHardback. Condizione: New. Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundations of the analysis of nonsmooth functions and optimization duality; and the back-propagation approach, relevant to neural networks.
Editore: Cambridge University Press, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
Lingua: Inglese
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 59,26
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Editore: Cambridge University Press, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
Lingua: Inglese
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EUR 55,30
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Editore: Cambridge University Press, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
Lingua: Inglese
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Aggiungi al carrelloHardcover. Condizione: New. Brand New! Fast Delivery Textbooks Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
Editore: Cambridge University Press, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
Lingua: Inglese
Da: Speedyhen, London, Regno Unito
EUR 44,71
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Aggiungi al carrelloHardcover. Condizione: Brand New. 227 pages. 9.25x6.25x0.75 inches. In Stock.
Editore: Cambridge University Press, Cambridge, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
Lingua: Inglese
Da: CitiRetail, Stevenage, Regno Unito
EUR 58,37
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundations of the analysis of nonsmooth functions and optimization duality; and the back-propagation approach, relevant to neural networks. Optimization techniques are at the core of data science. An understanding of the basic techniques and their fundamental properties provides important grounding for students, researchers, and practitioners. This compact, self-contained text covers the fundamentals of optimization algorithms, focusing on the techniques most relevant to data science. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Editore: Cambridge University Press, Cambridge, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 75,86
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundations of the analysis of nonsmooth functions and optimization duality; and the back-propagation approach, relevant to neural networks. Optimization techniques are at the core of data science. An understanding of the basic techniques and their fundamental properties provides important grounding for students, researchers, and practitioners. This compact, self-contained text covers the fundamentals of optimization algorithms, focusing on the techniques most relevant to data science. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
EUR 57,22
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Aggiungi al carrelloCondizione: New. Optimization techniques are at the core of data science. An understanding of the basic techniques and their fundamental properties provides important grounding for students, researchers, and practitioners. This compact, self-contained text covers the fundam.
Editore: Cambridge University Press, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 58,41
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Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundations of the analysis of nonsmooth functions and optimization duality; and the back-propagation approach, relevant to neural networks.
Editore: Cambridge University Press, GB, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
Lingua: Inglese
Da: Rarewaves.com UK, London, Regno Unito
EUR 62,17
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Aggiungi al carrelloHardback. Condizione: New. Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundations of the analysis of nonsmooth functions and optimization duality; and the back-propagation approach, relevant to neural networks.
Editore: Cambridge University Press, 2022
ISBN 10: 1316518981 ISBN 13: 9781316518984
Lingua: Inglese
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 48,51
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Aggiungi al carrelloHardback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Da: Revaluation Books, Exeter, Regno Unito
EUR 47,93
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Aggiungi al carrelloHardcover. Condizione: Brand New. 227 pages. 9.25x6.25x0.75 inches. In Stock. This item is printed on demand.