Sensorflow : Learning Through Motion
| dc.contributor.author | Ziebell, Nick | |
| dc.contributor.author | Abundez-Arce, Adrian | |
| dc.contributor.author | Johnson, Christopher R. | |
| dc.date.accessioned | 2019-01-31T22:33:58Z | |
| dc.date.available | 2019-01-31T22:33:58Z | |
| dc.date.issued | 2018-04 | |
| dc.description | Color poster with text, graphs, charts, and pictures. | en_US |
| dc.description.abstract | We want to enable the user to use internet-connected (IoT) devices in order to learn any alphabet outside the typical classroom setting in an engaging way. Machine learning facilitates classifying any type of images by learning through patterns in the data its given while making the program reusable for any type of similar datasets. Using this knowledge, this project is an app that will be able to discern different types of alphabets it knows using a Machine Learning model called Convolutional Neural Network. | en_US |
| dc.description.sponsorship | University of Wisconsin--Eau Claire Office of Research and Sponsored Programs. | en_US |
| dc.identifier.uri | http://digital.library.wisc.edu/1793/78941 | |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartofseries | USGZE AS589; | |
| dc.subject | Language learning | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Computer science | en_US |
| dc.subject | Mobile apps | en_US |
| dc.subject | Posters | |
| dc.title | Sensorflow : Learning Through Motion | en_US |
| dc.type | Presentation | en_US |
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