  In this paper we propose a method of obtaining points of extreme overfitting
- parameters of modern neural networks, at which they demonstrate close to 100
% training accuracy, simultaneously with almost zero accuracy on the test
sample. Despite the widespread opinion that the overwhelming majority of
critical points of the loss function of a neural network have equally good
generalizing ability, such points have a huge generalization error. The paper
studies the properties of such points and their location on the surface of the
loss function of modern neural networks.
