  In this work we approach attractor neural networks from a machine learning
perspective: we look for optimal network parameters by applying a gradient
descent over a regularized loss function. Within this framework, the optimal
neuron-interaction matrices turn out to be a class of matrices which correspond
to Hebbian kernels revised by a reiterated unlearning protocol. Remarkably, the
extent of such unlearning is proved to be related to the regularization
hyperparameter of the loss function and to the training time. Thus, we can
design strategies to avoid overfitting that are formulated in terms of
regularization and early-stopping tuning. The generalization capabilities of
these attractor networks are also investigated: analytical results are obtained
for random synthetic datasets, next, the emerging picture is corroborated by
numerical experiments that highlight the existence of several regimes (i.e.,
overfitting, failure and success) as the dataset parameters are varied.
