机器学习和深度学习技术

Yew Kee Wong
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引用次数: 2

摘要

在信息时代,决策者手头有大量的数据。大数据是指数据集不仅大,而且种类和速度也很高,这使得使用传统工具和技术难以处理。由于这些数据的快速增长,需要研究和提供解决方案,以便从这些数据集中处理和提取价值和知识。机器学习是一种数据分析方法,可以自动构建分析模型。它是人工智能的一个分支,基于这样一种理念:系统可以从数据中学习,识别模式,并在最少的人为干预下做出决策。这种最小的人为干预可以通过机器学习来提供,机器学习是先进的深度学习技术在大数据上的应用。本文旨在分析一些不同的机器学习和深度学习算法和方法,以及人工智能应用在各种决策领域提供的机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning and Deep Learning Technologies
In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Such minimal human intervention can be provided using machine learning, which is the application of advanced deep learning techniques on big data. This paper aims to analyse some of the different machine learning and deep learning algorithms and methods, aswell as the opportunities provided by the AI applications in various decision making domains.
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