The aim of the stochastic gradient descent (SGD) and its variants, is to approximate a local minimizer of a unknown function, which is revealed along the iterations. This talk intends to review convergence results in the case where the function is nonconvex and nondifferentiable. This includes almost-sure convergence, fluctuations, and avoidance of spurious critical points.