This activity is important because it is critical to know when the neural network stops learning. The goal of this activity is to demonstrate your understanding of when the neural network stops learning. For each statement, select if it is true or false. 1. The network stops learning when the user triggers early stopping. 2. The network stops learning when the weights are changing significantly. 3. The network stops learning when the system reaches its maximum run limits. 4. The network stops learning when the error (difference between actual and predicted values) is maximized. 5. The network stops learning when the input layer values are changed to zeros. 6. The network stops learning when the model prediction is no longer improving. This activity is important because it is critical to know when the neural network stops learning. The goal of this activity is to demonstrate your understanding of when the neural network stops learning. For each statement, select if it is true or false. 1. The network stops learning when the user triggers early stopping. 2. The network stops learning when the weights are changing significantly. 3. The network stops learning when the system reaches its maximum run limits. 4. The network stops learning when the error (difference between actual and predicted values) is maximized. 5. The network stops learning when the input layer values are changed to zeros. 6. The network stops learning when the model prediction is no longer improving