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 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

Given a learning rate of 0.01 and a gradient of 0.05, what is the update step for the weights?

What is the learning rate in backpropagation?Question 5Answera.The process of adjusting the weights and biases in the backward directionb.The process of minimizing the error between the predicted output and the actual outputc.The process of adjusting the weights and biases in the forward directiond.The hyperparameter that determines the size of the weight and bias updates

What is the main characteristic of the RProp algorithm?Select one:a.It uses a dynamic learning rateb.It uses a stochastic gradient descentc.It uses a predetermined set of weightsd.It uses a fixed learning rate