  We study the dynamics of supervised learning in layered neural networks, in
the regime where the size $p$ of the training set is proportional to the number
$N$ of inputs. Here the local fields are no longer described by Gaussian
probability distributions. We show how dynamical replica theory can be used to
predict the evolution of macroscopic observables, including the relevant
performance measures, incorporating the old formalism in the limit
$\alpha=p/N\to\infty$ as a special case. For simplicity we restrict ourselves
to single-layer networks and realizable tasks.
