- linar --> linear - curtosis --> kurtosis - exactely --> exactly - performace --> performance - featues --> features - # Here we use extract the two features and the label of the dataset --> # Here we extract the two features and the label of the dataset - seperation --> separation - seperate --> separate - # training of the model using the training data stored in X and Y for 4100 epochs --> # training of the model using the training data stored in X and Y - this values can vary --> these values can vary - descriped --> described - Definition of the network with two hidden layers --> Definition of the network with two layers (note: there is only 1 hidden layer) - paramters --> parameters - alot --> a lot - leraning --> learning
Here we use extract the two features and the label of the dataset --> # Here we extract the two features and the label of the dataset
training of the model using the training data stored in X and Y for 4100 epochs --> # training of the model using the training data stored in X and Y