HAR-stacked-residual-bidir-LSTMs
guillaume-chevalier
Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.
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3 data points · 2021-08-07 → 2022-02-20How do you feel about this project?
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Question copied to clipboardWhat is the guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs GitHub project? Description: "Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.". Written in Python. Explain what it does, its main use cases, key features, and who would benefit from using it.
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