Iteratively reducing all weights when there is a prediction error and increasing the weights when predicted output matches the expectation

Minimizing the prediction error by iteratively calculating the gradient of the cost function and modifying the network parameters to descend along the gradient

Iteratively reducing all bias inputs when there is a prediction error