File:Hinton Salakhutdinov 2006 Fig1.png

Fig. 1. Pretraining consists of learning a stack of restricted Boltzmann machines (RBMs), each having only one layer of feature detectors. The learned feature activations of one RBM are used as the ‘‘data’’ for training the next RBM in the stack. After the pretraining, the RBMs are ‘‘unrolled’’ to create a deep autoencoder, which is then fine-tuned using backpropagation of error derivatives. In: Geoffrey E. Hinton, and R. R. Salakhutdinov (2006). Reducing the dimensionality of data with neural networks. Science, 313:504-507. [ https://doi.org/10.1126/science.1127647 DOI: 10.1126/science.1127647]