STOCHASTIC OPTIMIZATION FOR LARGE-SCALE MACHINE LEARNING.

STOCHASTIC OPTIMIZATION FOR LARGE-SCALE MACHINE LEARNING.

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Bibliographic Details
Author / Creator:	CHAUHAN, VINOD KUMAR.
Imprint:	[S.l.] : CRC PRESS, 2021.
Description:	1 online resource
Language:	English
Subject:	Machine learning -- Statistical methods. Big data. Mathematical optimization. Stochastic processes. Stochastic Processes Big data Machine learning -- Statistical methods Mathematical optimization Stochastic processes
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/13693303

Hidden Bibliographic Details
ISBN:	9781000505610 1000505618 9781000505535 1000505537 9781003240167 100324016X 1032131756 9781032131757
Notes:	Includes bibliographical references and index.
Summary:	Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key research areas and recent research directions to solve large-scale machine learning problems. Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.
Other form:	Print version: 9781000505610 Print version: 1032131756 9781032131757
Standard no.:	10.1201/9781003240167

Description
Summary:	Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key research areas and recent research directions to solve large-scale machine learning problems. Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.
Physical Description:	1 online resource
Bibliography:	Includes bibliographical references and index.
ISBN:	9781000505610 1000505618 9781000505535 1000505537 9781003240167 100324016X 1032131756 9781032131757