Deep Learning Concepts in Operations Research
暫譯: 運籌學中的深度學習概念

Basu Mallik, Biswadip, Mukherjee, Gunjan, Kar, Rahul

  • 出版商: Auerbach Publication
  • 出版日期: 2024-08-30
  • 售價: $8,420
  • 貴賓價: 9.5$7,999
  • 語言: 英文
  • 頁數: 264
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1032553790
  • ISBN-13: 9781032553795
  • 相關分類: DeepLearning
  • 海外代購書籍(需單獨結帳)

商品描述

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.

商品描述(中文翻譯)

模型導向的方法在執行任務的分類和識別方面,促進了機器學習範式在多元技術領域的廣泛進展。《深度學習概念在運籌學中的應用》探討了這一模型導向方法的基礎概念。除了分類過程外,機器學習(ML)模型已經足夠有效,能夠預測各種現象的未來趨勢。物體分類、語音識別和臉部檢測等領域也廣泛應用人工智慧(AI)和機器學習(ML)。本書涵蓋了多個主題,包括:

- 應用和計算設備的概述
- 深度學習在人工智慧領域的影響
- 深度學習作為人工智慧的尖端方法
- 探索深度學習架構以提供尖端的人工智慧解決方案

運籌學是數學的一個分支,用於執行其他相關領域的多項操作任務,本書解釋了如何通過人工智慧和機器學習實施自動化策略來進行優化和參數選擇。運籌學對於決策制定有許多有益的方面。本書討論了如何正確的決策依賴於多個因素,並探討了人工智慧和機器學習如何用於建模方程式和定義約束,以更輕鬆地解決問題並發現合適且有效的解決方案。它還探討了自動化在減少人力勞動方面的重要角色,從而降低整體時間和成本。

作者簡介

Dr. Biswadip Basu Mallik is an associate professor of Mathematics in the Department of Basic Science & Humanities at Institute of Engineering & Management, University of Engineering & Management, Kolkata, India.

Dr. Gunjan Mukherjee is an associate professor in the Department of Computational Science, Brainware University, Barasat, India.

Rahul Kar holds a master's degree in mathematics from Burdwan University and is currently working as a SACT-II Mathematics faculty of Kalyani Mahavidyalaya, Kalyani, Nadia, West Bengal.

Aryan Chaudhary is the chief scientific advisor at BioTech Sphere Research, India, and a recognized researcher of healthcare and technology.

作者簡介(中文翻譯)

比斯瓦迪普·巴蘇·馬利克博士是印度加爾各答工程與管理大學基礎科學與人文學系的副教授。

甘詹·穆克吉博士是印度巴拉薩特Brainware大學計算科學系的副教授。

拉胡爾·卡爾擁有布爾德萬大學的數學碩士學位,目前在西孟加拉邦卡利亞尼的卡利亞尼大學擔任SACT-II數學教師。

阿里安·喬杜里是印度BioTech Sphere Research的首席科學顧問,並且是一位公認的醫療保健與技術研究者。