Machine learning algorithms are used in language processing, automated driving, and for prediction. Though the theory of machine learning has existed since the 1950s, it was not until the advent of advanced computing that their potential has begun to be realized. Gifted education is a field where machine learning has yet to be utilized, even though one of the underlying problems of gifted education is classification, which is an area where learning algorithms have become exceptionally accurate. We provide a brief overview of machine learning with a focus on neural networks and supervised learning, followed by a demonstration using simulated data and neural networks for classification issues with a practical explanation of the mechanics of the neural network and associated R code. Implications for gifted education are then discussed. Finally, the limitations of supervised learning are discussed. Code used in this article can be found at https://osf.io/4pa3b/

中文翻译：

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天才教育中的机器学习：使用神经网络的演示

机器学习算法用于语言处理、自动驾驶和预测。尽管机器学习理论自 1950 年代就已存在，但直到高级计算的出现才开始发挥其潜力。天才教育是一个尚未使用机器学习的领域，尽管天才教育的潜在问题之一是分类，这是学习算法变得异常准确的领域。我们提供了机器学习的简要概述，重点是神经网络和监督学习，然后是使用模拟数据和神经网络解决分类问题的演示，并对神经网络的机制和相关的 R 代码进行了实际解释。然后讨论了对天才教育的影响。最后，讨论了监督学习的局限性。本文中使用的代码可以在 https://osf.io/4pa3b/ 找到