How does the Quickprop algorithm improve upon traditional gradient descent algorithms?Select one:a.It uses a variable learning rateb.It uses a smaller learning ratec.It uses a fixed learning rated.It uses a larger learning rate

How does the Quickprop algorithm adjust the learning rate for each weight in the neural network?Select one:a.It adjusts the learning rate based on the previous weight updateb.It uses a fixed learning rate for all weightsc.It uses a variable learning rate for all weightsd.It uses a fixed learning rate for some weights and a variable learning rate for others

What is the main advantage of the Quickprop algorithm over the backpropagation algorithm?Select one:a.It is more accurateb.It is less sensitive to initializationc.It is more efficientd.It is faster to converge

How does the Quickprop algorithm handle weight updates that are too large?Question 11Answera.It reduces the weight updatesb.It discards the weight updatesc.It increases the learning rated.It reduces the learning rate

What is the main disadvantage of the Quickprop algorithm?Select one:a.It is more complex than other algorithmsb.It requires more computational resourcesc.It is less efficient than other algorithmsd.It is more sensitive to initialization

What is the Quickprop algorithm used for?Select one:a.Data analysisb.Machine learningc.Neural network trainingd.Data visualization