How can the learning process be stopped in the backpropagation rule?Question 2Answera.There is convergence involved.b.  No heuristic criteria exist.c.Noned.  Based on the average gradient value

The learning process in the backpropagation rule can be stopped through several methods:

a. Convergence: This is when the error rate of the model stops decreasing significantly or remains constant over iterations. This indicates that the model has learned as much as it can from the training data and further training might lead to overfitting.

b. No heuristic criteria exist: This statement is not entirely accurate. There are heuristic criteria that can be used to stop the learning process such as setting a maximum number of epochs (iterations over the entire dataset), a minimum error rate, or a minimum change in error rate.

c. None: This option suggests that there is no way to stop the learning process, which is not accurate.

d. Based on the average gradient value: This is another valid method. If the average gradient value is very small, it means that the model is not learning much from the data and the learning process can be stopped. This is because a small gradient value indicates that the model is near a local or global minimum of the loss function.