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Autoencoders’ Example Uses Augment Data for Machine Learning
Autoencoders are a type of unsupervised AI network that reduce data dimensions and enable developers and data scientists to seamlessly improve the accuracy of algorithms. Developers and data scientists use autoencoders for data denoising, nonlinear dimensionality reduction, sequence-to-sequence prediction, and feature extraction. Through autoencoder technology, data scientists can optimize their workflow and augment machine learning projects.
In this new article in TechTarget, Russ Felker, GlobalTranz’s CTO, joins a host of technology experts from AIM Consulting, SPR, Cognizant Appen, and One Stream Software, to share insights into how developers and data scientists can apply autoencoder technology to augment data for real-world application.
GlobalTranz is utilizing autoencoders as a feature extractor and to perform denoising. Autoencoders have enabled GlobalTranz’s technology team to more efficiently complete data classification. “By Grouping like items together, you are enabling the system to make fast recommendations on what the output should be,” Felker said.
To read the article, please click here.
The post Autoencoders’ Example Uses Augment Data for Machine Learning appeared first on GlobalTranz.
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