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  1. Bog
  2. Optimalisering
  3. Lineær programmering

Optimization for Data Analysis

Af: Benjamin Recht, Stephen J. Wright Engelsk Hardback

Optimization for Data Analysis

Af: Benjamin Recht, Stephen J. Wright Engelsk Hardback
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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.
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Tjek vores konkurrenters priser
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.
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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 238
ISBN-13: 9781316518984
Indbinding: Hardback
Udgave:
ISBN-10: 1316518981
Kategori: Lineær programmering
Udg. Dato: 21 apr 2022
Længde: 18mm
Bredde: 236mm
Højde: 156mm
Forlag: Cambridge University Press
Oplagsdato: 21 apr 2022
Forfatter(e): Benjamin Recht, Stephen J. Wright
Forfatter(e) Benjamin Recht, Stephen J. Wright

Kategori Lineær programmering

ISBN-13 9781316518984

Sprog Engelsk

Indbinding Hardback

Sider 238

Udgave

Længde 18mm

Bredde 236mm

Højde 156mm

Udg. Dato 21 apr 2022

Oplagsdato 21 apr 2022

Forlag Cambridge University Press

Kategori sammenhænge