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Title: LSTM-Human-Activity-Recognition-master Download
 Description: Compared with the classical methods, the recursive neural network (RNN) with long-term memory cells does not need or almost need feature engineering. Data can be directly input into the neural network, which acts as a black box and can correctly model the problem. Other research can use a lot of Feature Engineering on activity recognition data sets, which is a signal processing method combined with classical data science and technology. The method here is very simple in terms of the number of data preprocessing
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LSTM-Human-Activity-Recognition-master 0  2019-04-16

LSTM-Human-Activity-Recognition-master\.gitignore 24  2019-04-16

LSTM-Human-Activity-Recognition-master\LICENSE 1086  2019-04-16

LSTM-Human-Activity-Recognition-master\LSTM.ipynb 213291  2019-04-16

LSTM-Human-Activity-Recognition-master\LSTM_files 0  2019-04-16

LSTM-Human-Activity-Recognition-master\LSTM_files\LSTM_16_0.png 77480  2019-04-16

LSTM-Human-Activity-Recognition-master\LSTM_files\LSTM_18_1.png 43286  2019-04-16

LSTM-Human-Activity-Recognition-master\README.md 30154  2019-04-16

LSTM-Human-Activity-Recognition-master\data 0  2019-04-16

LSTM-Human-Activity-Recognition-master\data\.gitignore 33  2019-04-16

LSTM-Human-Activity-Recognition-master\data\download_dataset.py 914  2019-04-16

LSTM-Human-Activity-Recognition-master\data\source.txt 2068  2019-04-16

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