The training procedure of choice often is Stochastic Gradient Descent (SGD). It consists of a series of iterations where we update the parameters according to the equation
$$ \begin{align*} \boldsymbol{\Theta}_{k+1} = \boldsymbol{\Theta}_k - \eta \nabla \mathcal{C} (\boldsymbol{\Theta}_k) \end{align*} $$at each \( k \)-th iteration. There are a range of variants of the algorithm which aim at making the learning rate \( \eta \) more adaptive so the method might be more efficient while remaining stable.