Training a restricted Boltzmann machine on a GPU with TensorFlow

During the second half of the last decade, researchers have started to exploit the impressive capabilities of graphical processing units (GPUs) to speed up the execution of various machine learning algorithms (see for instance [1] and [2] and the references therein). Compared to a standard CPU, modern GPUs offer a breathtaking degree of parallelization - … Continue reading Training a restricted Boltzmann machine on a GPU with TensorFlow

Learning algorithms for restricted Boltzmann machines – contrastive divergence

In the previous post on RBMs, we have derived the following gradient descent update rule for the weights. $latex \Delta W_{ij} = \beta \left[ \langle v_i \sigma(\beta a_j) \rangle_{\mathcal D} - \langle v_i \sigma(\beta a_j) \rangle_{P(v)} \right] &s=1$ In this post, we will see how this update rule can be efficiently implemented. The first thing … Continue reading Learning algorithms for restricted Boltzmann machines – contrastive divergence

Restricted Boltzmann machines

In the previous post, we have seen that a Boltzmann machine as studied so far suffers from two deficiencies. First, training is very slow as we have to run a Gibbs sampler until convergence for every iteration of the gradient descent algorithm. Second, we can only see the second moments of the data distribution and … Continue reading Restricted Boltzmann machines