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About RBM, DBN, and DBM
This document discusses Restricted Boltzmann Machines (RBMs) and their learning algorithm, contrastive divergence (CD). It begins by introducing energy-based models and Boltzmann machines. It then describes RBMs as undirected graphical models with hidden and visible units. The learning algorithm, CD, approximates maximum likelihood training by running Gibbs sampling for a few steps from the data to estimate model expectations, rather than running it to convergence. This provides a faster way to estimate the gradient of the log-likelihood than fully averaging over the model distribution. CD works well in practice for pre-training deep networks by learning good feature representations from data in an unsupervised manner.
Detailed Information
| Author: | ['ali'] |
|---|---|
| Publication Year: | 2018 |
| Pages: | 79 |
| Language: | English |
| Format: | |
| Price: | FREE |
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