A dataset of deep learning performance from cross-base data encoding on MNIST and MNIST-C.

IF 1 Q3 MULTIDISCIPLINARY SCIENCES
Data in Brief Pub Date : 2024-12-03 eCollection Date: 2024-12-01 DOI:10.1016/j.dib.2024.111194
Lawrence McKnight, Chandra Jaiswal, Issa AlHmoud, Balakrishna Gokaraju
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引用次数: 0

Abstract

Effective data representation in machine learning and deep learning is paramount. For an algorithm or neural network to capture patterns in data and be able to make reliable predictions, the data must appropriately describe the problem domain. Although there exists much literature on data preprocessing for machine learning and data science applications, novel data representation methods for enhancing machine learning model performance remain highly absent within the literature. This dataset is a compilation of convolutional neural network model performance trained and tested on a wide range of numerical base representations of the MNIST and MNIST-C datasets. This performance data can be further analysed by the research community to uncover trends in model performance against the numerical base of its data. This dataset can be used to produce more research of the same nature, testing cross-base data encoding on machine learning training and testing data for a wide range of real-world applications.

基于MNIST和MNIST- c的跨基数据编码的深度学习性能数据集。
有效的数据表示在机器学习和深度学习中是至关重要的。为了让算法或神经网络捕获数据中的模式并能够做出可靠的预测,数据必须恰当地描述问题域。虽然有很多关于机器学习和数据科学应用的数据预处理的文献,但用于增强机器学习模型性能的新颖数据表示方法在文献中仍然非常缺乏。该数据集是卷积神经网络模型性能的汇编,在MNIST和MNIST- c数据集的广泛数值基础表示上进行了训练和测试。研究团体可以进一步分析这些性能数据,以根据数据的数值基础揭示模型性能的趋势。该数据集可用于产生更多相同性质的研究,在机器学习训练和广泛的现实世界应用中测试跨基数据编码。
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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