Deep Learning Concepts in Operations Research

The book provides mathematicians an overview of AI and machine learning relevant to operations research. It focuses decision modeling and optimization models as well as algorithms.

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines:

• An overview of applications and computing devices
• Deep learning impacts in the field of AI
• Deep learning as state-of-the-art approach to AI
• Exploring deep learning architecture for cutting-edge AI solutions

Operations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

Contributor: Biswadip Basu Mallik (Edited by), Gunjan Mukherjee (Edited by), Rahul Kar (Edited by), Aryan Chaudhary (Edited by)

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