  Nowadays, this is very popular to use the deep architectures in machine
learning. Deep Belief Networks (DBNs) are deep architectures that use stack of
Restricted Boltzmann Machines (RBM) to create a powerful generative model using
training data. DBNs have many ability like feature extraction and
classification that are used in many applications like image processing, speech
processing and etc. This paper introduces a new object oriented MATLAB toolbox
with most of abilities needed for the implementation of DBNs. In the new
version, the toolbox can be used in Octave. According to the results of the
experiments conducted on MNIST (image), ISOLET (speech), and 20 Newsgroups
(text) datasets, it was shown that the toolbox can learn automatically a good
representation of the input from unlabeled data with better discrimination
between different classes. Also on all datasets, the obtained classification
errors are comparable to those of state of the art classifiers. In addition,
the toolbox supports different sampling methods (e.g. Gibbs, CD, PCD and our
new FEPCD method), different sparsity methods (quadratic, rate distortion and
our new normal method), different RBM types (generative and discriminative),
using GPU, etc. The toolbox is a user-friendly open source software and is
freely available on the website
http://ceit.aut.ac.ir/~keyvanrad/DeeBNet%20Toolbox.html .
