A Comparison of Four Open Source Multi-Layer Perceptrons for Neural Network Neophytes

机译：神经网络新植物的四种开源多层感知器比较

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Neural networks and specifically Mutli-Layer Perceptrons can be used to classify data into categories or groups. The Multi-Layer Perceptron learns by processing training data. After training, the Multi-Layer Perceptron can make predictions about new data which it hadn’t seen before. There has been extensive research about Multi-Layer Perceptrons but the research is often aimed at people creating and researching neural networks. This paper is aimed at people who want to just use an open source Multi-Layer Perceptron for a project and have no real understanding or interest on how they work. The paper compares four open source Multi-Layer Perceptrons. Three different datasets are processed using similar configurations. The results are presented and conclusions are provided.

机译：神经网络，特别是多层感知器，可用于将数据分类为类别或组。多层感知器通过处理训练数据进行学习。经过训练后，多层感知器可以对以前从未见过的新数据做出预测。关于多层感知器已经进行了广泛的研究，但是该研究通常针对创建和研究神经网络的人们。本文针对那些只想使用开源多层感知器来完成项目的人员，而他们对其工作方式没有真正的了解或兴趣。本文比较了四个开源多层感知器。使用相似的配置处理三个不同的数据集。给出结果并提供结论。

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《International Conference on Advances in Big Data, Computing and Data Communication Systems》|2019年|1-5|共5页
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Laurie Butgereit; Laura Martinus;
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Biological neural networks; Neurons; Iris; Java; Licenses; Python; Libraries;

机译：生物神经网络;神经元;虹膜; Java;许可证; Python;图书馆;

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A Comparison of Four Open Source Multi-Layer Perceptrons for Neural Network Neophytes

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