The Restricted Boltzman Machine provides a simple way to learn a layer of features without supervision. Many layers of representation can be learned by treating the hidden states of one RBM as the visible data for training the next RBM. This creates good generative models that can be fine-tuned. Back preparation can be used for labels and discrimination.