BOOKS - Deep Learning Applications in Operations Research
Deep Learning Applications in Operations Research - Aryan Chaudhary, Biswadip Basu Mallik, Gunjan Mukherjee, Rahul Kar 2025 PDF | EPUB CRC Press BOOKS
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Deep Learning Applications in Operations Research
Author: Aryan Chaudhary, Biswadip Basu Mallik, Gunjan Mukherjee, Rahul Kar
Year: 2025
Pages: 275
Format: PDF | EPUB
File size: 50.4 MB
Language: ENG



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DEEP LEARNING APPLICATIONS IN OPERATIONS RESEARCH The rapid development of deep learning technology has had a profound impact on various fields, including operations research. The ability of deep learning algorithms to analyze large amounts of data and identify complex patterns has made them an essential tool for solving complex problems in this field. This book provides a comprehensive overview of the applications of deep learning in operations research, highlighting its potential to revolutionize the way we approach decision-making and problem-solving in various industries. Understanding the Evolution of Technology To fully appreciate the power of deep learning in operations research, it is important to first understand the evolution of technology. From the early days of simple calculators to the sophisticated machines of today, technology has come a long way. Each step in this journey has been driven by the need to improve efficiency, productivity, and accuracy. As we move forward, it is crucial that we continue to develop a personal paradigm for perceiving the technological process of developing modern knowledge. This will enable us to harness the full potential of deep learning and other advanced technologies to ensure the survival of humanity and the unification of people in a warring state. The Need for Deep Learning Applications in Operations Research Operations research is a field that deals with the application of mathematical and computational methods to optimize decision-making processes. It involves analyzing complex systems and finding solutions to complex problems. With the increasing amount of data available, traditional methods are no longer sufficient to handle the complexity of these systems. This is where deep learning comes in, providing a powerful tool for analyzing large amounts of data and identifying patterns that were previously undetectable.
ПРИЛОЖЕНИЯ ДЛЯ ГЛУБОКОГО ОБУЧЕНИЯ В ИССЛЕДОВАНИЯХ ОПЕРАЦИЙ Быстрое развитие технологии глубокого обучения оказало глубокое влияние на различные области, включая исследования операций. Способность алгоритмов глубокого обучения анализировать большие объёмы данных и выявлять сложные закономерности сделала их важнейшим инструментом для решения сложных задач в этой области. Эта книга содержит всесторонний обзор применения глубокого обучения в исследованиях операций, подчеркивая его потенциал революционизировать подход к принятию решений и решению проблем в различных отраслях. Понимание эволюции технологий Чтобы полностью оценить возможности глубокого обучения в исследованиях операций, важно сначала понять эволюцию технологий. От первых дней простых калькуляторов до современных сложных машин технологии прошли долгий путь. Каждый шаг в этом путешествии был обусловлен необходимостью повышения эффективности, производительности и точности. По мере продвижения вперед крайне важно, чтобы мы продолжали развивать личную парадигму восприятия технологического процесса развития современных знаний. Это позволит нам использовать весь потенциал глубокого обучения и других передовых технологий для обеспечения выживания человечества и объединения людей в воюющем государстве. Потребность в приложениях глубокого обучения в исследованиях операций Исследование операций - это область, которая занимается применением математических и вычислительных методов для оптимизации процессов принятия решений. Она предполагает анализ сложных систем и поиск решений сложных задач. С увеличением объема доступных данных традиционных способов уже недостаточно для управления сложностью этих систем. Именно здесь приходит глубокое обучение, предоставляя мощный инструмент для анализа больших объемов данных и выявления закономерностей, которые ранее не обнаруживались.
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