How does scaled conjugate gradient work in neural network training? Comparison with gradient descent

I am very new and beginner in the machine learning world, and I would like to ask if someone could simply explain to me how does the scaled conjugate gradient method work in neural network training? Especially in comparison with the gradient descent method, because I already understand that one.

I know exactly the steps on how to train a neural network with gradient descent, but in relation to scaled gradient I can only find far too advanced explanations that I can’t yet understand.