What is the RProp algorithm's weight update rule?Question 19Answera.The weight update is inversely proportional to the derivative of the error functionb.The weight update is proportional to the derivative of the error functionc.The weight update is inversely proportional to the second 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?Question 16Answera.The learning rate is updated based on the difference between the current and previous iteration's errorb.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 gradientd.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 bias update rule in the backpropagation algorithm?Question 18Answera.The mathematical formula that is used to update the biases based on the gradient descentb.The process of adjusting the weights and biases in the forward directionc.The process of minimizing the error between the predicted output and the actual outputd.The process of adjusting the weights and biases in the backward direction

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)