Restricted　Boltzmann　Machine　is　a　fundamental　method　in　deep　learning　networks.　Training　and　generalization　is　an　ill-defined　problem　in　that　many　different　networks　may　achieve　the　training　goal;　however　each　will　respond　differently　to　an　unknown　input.　Traditional　approaches　include　stopping　the　training　early　and/or　restricting　the　size　of　the　network.　These　approaches　ameliorate　the　problem　of　over-fitting　where　the　network　learns　the　patterns　presented　but　is　unable　to　generalize.　Bayesian　regularization　addresses　these　issues　by　requiring　the　weights　of　the　network　to　attain　a　minimum　magnitude.　This　ensures　that　non-contributing　weights　are　reduced　significantly　and　the　resulting　network　represents　the　essence　of　the　inter-relations　of　the　training.　Bayesian　Regularization　simply　introduces　an　additional　term　to　the　objective　function.　This　term　comprises　the　sum　of　the　squares　of　the　weights.　The　optimization　process　therefore　not　only　achieves　the　objective　of　the　original　cost　(i.e.　the　minimization　of　an　error　metric)　but　it　also　ensures　that　this　objective　is　achieved　with　minimum-magnitude　weights.　We　have　introduced　Bayesian　Regularization　in　the　training　of　Restricted　Boltzmann　Machines　and　have　applied　this　method　in　experiments　of　hand-written　numbers　classification.　Our　experiments　showed　that　by　adding　Bayesian　regularization　in　the　training　of　RBMs,　we　were　able　to　improve　the　generalization　capabilities　of　the　trained　network　by　reducing　its　recognition　errors　by　more　than　1.6%.