In order to simplify the process, we have made some operative decisions. For instance, since precision of learning is not important (the objective is to capture the overall distribution), we do not wait for convergence of the algorithm and simply update the learning rule 1000 times. We use a learning rate μ = 0.01 and compute 1000N samples for each learning step, being N the size of the system. For simplicity also, instead of the critical temperature of the lattice Ising model, we set an arbitrary inverse temperature of β = 1. Note that the choice of operating temperature is irrelevant, since it only implies a rescaling of the parameters that the algorithm will compensate.