  We study the training of finite-width two-layer smoothed ReLU networks for
binary classification using the logistic loss. We show that gradient descent
drives the training loss to zero if the initial loss is small enough. When the
data satisfies certain cluster and separation conditions and the network is
wide enough, we show that one step of gradient descent reduces the loss
sufficiently that the first result applies.
