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description
This was my Master's project where i was involved using a dataset from Wireless Sensor Data Mining Lab (WISDM) to build a machine learning model to predict basic human activities using a smartphone accelerometer, Using Tensorflow framework, recurrent neural nets and multiple stacks of Long-short-term memory units(LSTM) for building a deep network. After the model was trained, it was saved and exported to an android application and the predictions were made using the model and the interface to speak out the results using text-to-speech API.
description
The HTML5 Creation Engine: Create beautiful digital content with the fastest, most flexible 2D WebGL renderer.
homepage
homepage
http://pixijs.com
url
https://githubhelp/iamshant/human-activity-recognition-using-recurrent-neural-nets-rnn-lstm-and-tensorflow-on-smartphones
url
https://githubhelp/pixijs/pixijs
disk Usage
145845
dis kUsage
108256
stargazer Count
0
stargazer Count
43266
forkCount
0
forkCount
4745
watcher Count
0
watcher Count
992
created At
2020-12-21
created At
2013-01-22
license Info
license Info
MIT License
issues
0
issues
50
languages
1
languages
5
repository Topics
0
repository Topics
10
owner
iamshant
owner
pixijs
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owner Avatar
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other

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