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"source" : [
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" # Graph Data (optional)\n " ,
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" \n " ,
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- " We'll graph the input files on two separate graphs, acceleration and gyroscope, as each data set has different units and scale"
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+ " We'll graph the input files on two separate graphs, acceleration and gyroscope, as each data set has different units and scale. "
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]
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},
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{
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"source" : [
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" ## Parse and prepare the data\n " ,
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" \n " ,
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- " The next cell parses the csv files and transforms them to a format that will be used to train the full connected neural network.\n " ,
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+ " The next cell parses the csv files and transforms them to a format that will be used to train the fully connected neural network.\n " ,
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" \n " ,
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" Update the `GESTURES` list with the gesture data you've collected in `.csv` format.\n "
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]
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"source" : [
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" ## Randomize and split the input and output pairs for training\n " ,
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" \n " ,
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- " Randomly sprint the input and ouput pairs into sets of data. 60% for training, 20% for validation, and 20% for testing.\n " ,
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+ " Randomly split input and output pairs into sets of data: 60% for training, 20% for validation, and 20% for testing.\n " ,
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" \n " ,
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" - the training set is used to train the model\n " ,
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- " - the validatiin set is used to measure how well the model is performing during training\n " ,
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- " - the testing set is used test the model after training"
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+ " - the validation set is used to measure how well the model is performing during training\n " ,
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+ " - the testing set is used to test the model after training"
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]
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},
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{
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"source" : [
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" ### Graph the mean absolute error\n " ,
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" \n " ,
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- " [Mean absolute error](https://en.wikipedia.org/wiki/Mean_absolute_error), is another metric to judge the performance of the model.\n " ,
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+ " [Mean absolute error](https://en.wikipedia.org/wiki/Mean_absolute_error) is another metric to judge the performance of the model.\n " ,
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" \n "
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]
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},
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]
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}
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]
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- }
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+ }
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