What is the RProp algorithm's weight update rule?Select one:a.The weight update is inversely proportional to the second derivative of the error functionb.The weight update is inversely proportional to the derivative of the error functionc.The weight update is proportional to the derivative of the error functiond.The weight update is proportional to the second derivative of the error function

What is the RProp algorithm's learning rate update rule?Select one:a.The learning rate is updated based on the difference between the current and previous iteration's gradientb.The learning rate is updated based on the difference between the current and previous iteration's weightsc.The learning rate is updated based on the difference between the current and previous iteration's errord.The learning rate is updated based on the difference between the current and previous iteration's Hessian matrix

How does the RProp algorithm adjust the learning rate?Select one:a.It uses a fixed learning rate regardless of the errorb.It increases the learning rate if the error decreases and decreases the learning rate if the error increasesc.It increases the learning rate if the error increases and decreases the learning rate if the error decreasesd.It uses a predetermined set of learning rates for each iteration

What is the process of adjusting the weights and biases based on the error in the output layer called?Select one:a.Backpropagationb.None of the abovec.Forward propagationd.Activation

What is the weight update rule in backpropagation?Select one:a.W(i, j) = W(i, j) - alpha * delta(i) * output(j)b.W(i, j) = W(i, j) + alpha * delta(i) * output(j)c.W(i, j) = W(i, j) / alpha * delta(i) * output(j)d.W(i, j) = W(i, j) * alpha * delta(i) * output(j)

The weights are kept constant to avoid overfitting           The weights are adjusted proportionally based on the error gradient           The weights are increased by a fixed amount           The weights are decreased by a fixed amount