Sensorflow : Learning Through Motion

dc.contributor.authorZiebell, Nick
dc.contributor.authorAbundez-Arce, Adrian
dc.contributor.authorJohnson, Christopher R.
dc.date.accessioned2019-01-31T22:33:58Z
dc.date.available2019-01-31T22:33:58Z
dc.date.issued2018-04
dc.descriptionColor poster with text, graphs, charts, and pictures.en_US
dc.description.abstractWe 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.sponsorshipUniversity of Wisconsin--Eau Claire Office of Research and Sponsored Programs.en_US
dc.identifier.urihttp://digital.library.wisc.edu/1793/78941
dc.language.isoen_USen_US
dc.relation.ispartofseriesUSGZE AS589;
dc.subjectLanguage learningen_US
dc.subjectMachine learningen_US
dc.subjectComputer scienceen_US
dc.subjectMobile appsen_US
dc.subjectPosters
dc.titleSensorflow : Learning Through Motionen_US
dc.typePresentationen_US

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