A quickly converging good solution, rather than a slowly converging best solution, usually is sufficient for most problems. Training a Neural Network To properly train a neural network, the developer feeds the model a variety of real-life examples, called training sets. The data sets normally contain input data and output data. The neural network creates connections and learns patterns based on these input and output data sets. Each pattern creates a unique configuration of network structure with a unique set of connection strengths or weights.
Neural networks learn new tasks much like people learn to throw a basketball through the hoop.
Ballplayers throw a basketball toward the hoop. If the ball goes in, it doesn't matter whether it hit the backboard, the rim, or nothing but the net. The task has been performed within the game's tolerances. However, if the ball does not go through the hoop, ballplayers adjust their stance, the force with which they throw the ball, or their aim. Similarly, if the neural network does not match the pattern within the given tolerance, it will adjust and try again.