损失函数在人工智能中的重要性

Nodir Raximov, Jura Kuvandikov, Khasanov Dilmurod
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引用次数: 1

摘要

众所周知,人工智能是基于机器学习(ML)和深度学习(DL)。改进ML和DL与神经网络中的Loss函数有关。在重要性优化算法中,损失函数具有重要的意义。在人工智能中有不同类型的损失函数。本文阐述了几种损耗类型的优点(缺点),并结合实例进行了分析。我们可能会寻求最大化或最小化目标函数,这意味着我们正在寻找分别具有最高或最低分数的候选解决方案。通常,神经网络是人工智能的主要组成部分之一,我们寻求最小化误差。因此,目标函数通常被称为成本函数或损失函数,由损失函数计算的值被简单地称为“损失”。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The importance of loss function in artificial intelligence
As known, Artificial Intelligence is based on Machine Learning(ML) and Deep Learning(DL). And improving ML and DL is connected to Loss function in neural networks. In the importance optimization algorithms Loss function of great importance. There are different types of Loss function in Artificial Intelligence. In this article, some Loss types are illustrated advantages (disadvantages) and analyzed with examples. We may seek to maximize or minimize the objective function, meaning that we are searching for a candidate solution that has the highest or lowest score respectively. Typically, with neural networks that are one of the main part of the Artificial intelligence, we seek to minimize the error. As such, the objective function is often referred to as a cost function or a loss function and the value calculated by the loss function is referred to as simply “Loss.”
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