How does a Deep Neural Network work?

The purpose of the iterations is to train and update the weights of your network (iteratively).

  • So you can incorporate more information (e.g. data w/ new labels) into the training process through iterations, so that you eventually can find a minimum of your cost function.
  • In fact you want to reach your global optimum. Through the iterations you would navigate from your net w/ initialised weights (usually high loss, not even close to an optimum) to a much better parametrization (= weights) which is much more close to the optimum, where your costs are minimised.