Handbook of Machine Learning Volume 2 Optimization and Decision Making

This book PDF is perfect for those who love Computers genre, written by Tshilidzi Marwala and published by World Scientific Publishing Company which was released on 29 October 2019 with total hardcover pages 320. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Handbook of Machine Learning Volume 2 Optimization and Decision Making books below.

Handbook of Machine Learning Volume 2 Optimization and Decision Making

Author	: Tshilidzi Marwala
File Size	: 40,7 Mb
Publisher	: World Scientific Publishing Company
Language	: English
Release Date	: 29 October 2019
ISBN	: 9811205663
Pages	: 320 pages

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Handbook of Machine Learning Volume 2 Optimization and Decision Making by Tshilidzi Marwala Book PDF Summary

Building on Handbook of Machine Learning - Volume 1: Foundation of Artificial Intelligence, this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be used to define, disprove or validate economic modeling and decision making concepts.

Handbook of Machine Learning Volume 2 Optimization and Decision Making by Tshilidzi Marwala,Collins Achepsah Leke

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Handbook Of Machine Learning Volume 2 Optimization And Decision Making by Tshilidzi Marwala,Collins Achepsah Leke

Building on , this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be

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Handbook of Machine Learning by Tshilidzi Marwala,Collins Achepsah Leke

Download or read online Handbook of Machine Learning written by Tshilidzi Marwala,Collins Achepsah Leke, published by Unknown which was released on 2020. Get Handbook of Machine Learning Books now! Available in PDF, ePub and Kindle.

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Optimization in Machine Learning and Applications by Anand J. Kulkarni,Suresh Chandra Satapathy

This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations

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Rational Machines and Artificial Intelligence by Tshilidzi Marwala

Intelligent machines are populating our social, economic and political spaces. These intelligent machines are powered by Artificial Intelligence technologies such as deep learning. They are used in decision making. One element of decision making is the issue of rationality. Regulations such as the General Data Protection Regulation (GDPR) require that

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Machine Learning for Decision Makers by Patanjali Kashyap

Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the

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Optimization for Machine Learning by Suvrit Sra,Sebastian Nowozin,Stephen J. Wright

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to

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Handbook of Machine Learning for Computational Optimization by Vishal Jain,Sapna Juneja,Abhinav Juneja,Ramani Kannan

Focuses on new machine learning developments that can lead to newly developed applications Uses a predictive and futuristic approach which makes Machine Learning a promising tool for business processes and sustainable solutions Promotes newer algorithms which are more efficient and reliable for a new dimension in discovering certain latent domains

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