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Activity-Recognition-Using-Neural-Networks

• Built a model using the keras library and dpylr package in R to predict the type of activity a person is doing.

• Used the ReLU activation function and trained the network for 100 epochs using batch gradient descent.

• Fitted the trained model to the test dataset and computed the balanced accuracy, per-class sensitivity, and specificity.

• Performed mini batch gradient descent and retrained the model.

• Added a new hidden layer to the network and documented the changes on the performance of the model.

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