深度分析机器学习和大数据在推动数字营销范式转变中的作用

Mano Ashish Tripathi, Ravikesh Tripathi, Femmy Effendy, Geetha Manoharan, M. John Paul, Mohd Aarif
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引用次数: 2

摘要

机器学习(ML)是一种人工神经网络(ANN),可以帮助开发人员在获得所需的所有数据之前提高软件的预测能力。由于信息是无价之宝,因此向完全自主代理的发展需要更好的方法来管理目前无处不在的内容基础设施。从医学诊断到数据呈现和操作,再到科学研究,各种领域都受益于计算机视觉和人工智能的进步。从受污染或错误的数据中学习可能是昂贵的,就像体育训练对那些容易受伤的人来说是危险的一样。正如《接近数据科学》中所解释的那样,如果算法教授不当,组织将产生成本,而不是看到收益。组织需要能够验证任何大型数据集的质量和一致性,以及它们的来源,以确保任何算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An In-Depth Analysis of the Role That ML and Big Data Play in Driving Digital Marketing’s Paradigm Shift
Machine learning (ML) is an artificial neural network (ANN) that helps developers improve their software’s predictive abilities before they have all the data they need. Because information is so priceless, progress toward fully autonomous agents requires better methods for managing the omnipresent content infrastructures that exist today. All sorts of fields have benefited from advancements in computer vision and AI, from medical diagnosis to data presentation and operations to scientific study, and so on. Learning from polluted or erroneous data may be expensive, much as training for a sport can be dangerous to those who are vulnerable to injury. An organization will incur costs rather than see benefits if its algorithms are improperly taught, as explained in Approaching Data Science. Organizations need to be able to verify the quality and consistency of any large datasets, as well as their sources, to ensure the efficacy of any algorithm.
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