Restricted Boltzmann machines (RBMs) are the first neural networks used for unsupervised learning, created by Geoff Hinton (university of Toronto).
The aim of RBMs is to find patterns in data by reconstructing the inputs using only two layers (the visible layer and the hidden layer). By moving forward an RBM translates the visible layer into a set of numbers that encodes the inputs, in backward pass it takes those set of numbers and translates them to the visible layer to regenerate the inputs.
In this code we introduce to you very simple algorithms that depend on contrastive divergence training. The details of this method are explained step by step in the comments inside the code.
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To learn about RBM :
BERGHOUT Tarek (2019). Restricted Boltzmann Machine (https://www.mathworks.com/matlabcentral/fileexchange/71212-restricted-boltzmann-machine), MATLAB Central File Exchange. Retrieved .
new descriptif image
in the last code we trained by mistake the RBM with scalar units in visible and hidden layers, as we change the representation of these units into binary units during training and we'v got a much more improvements in accurcy
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