Difference between revisions of "Restricted Boltzmann Machine (RBM)"
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| + | [http://www.youtube.com/results?search_query=Restricted+Boltzmann+Machines+RBM YouTube search...] | ||
| + | [http://www.google.com/search?q=Restricted+Boltzmann+Machines+RBM+machine+learning+ML+artificial+intelligence ...Google search] | ||
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| + | * [http://deeplearning4j.org/restrictedboltzmannmachine.html Guide] | ||
| + | * [[Clustering]] | ||
| + | * [http://pathmind.com/wiki/restricted-boltzmann-machine A Beginner's Guide to Restricted Boltzmann Machines (RBMs) | Chris Nicholson - A.I. Wiki pathmind] | ||
| + | * [[Deep Belief Network (DBN)]] | ||
| + | * [[Variational Autoencoder (VAE)]] | ||
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| + | Useful for dimensionality reduction, classification, [[Regression]], collaborative filtering, feature learning and topic modeling. RBMs are shallow, two-layer neural nets that constitute the building blocks of deep-belief networks. The biggest difference between Boltzmann Machine (BM) and RBMs is that RBMs are a better usable because they are more restricted. They don’t trigger-happily connect every neuron to every other neuron but only connect every different group of neurons to every other group, so no input neurons are directly connected to other input neurons and no hidden to hidden connections are made either. RBMs can be trained like FFNNs with a twist: instead of passing data forward and then back-propagating, you forward pass the data and then backward pass the data (back to the first layer). After that you train with forward-and-back-propagation. Smolensky, Paul. Information processing in dynamical systems: Foundations of harmony theory. No. CU-CS-321-86. COLORADO UNIV AT BOULDER DEPT OF COMPUTER SCIENCE, 1986. | ||
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| + | http://www.asimovinstitute.org/wp-content/uploads/2016/09/rbm.png | ||
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Revision as of 16:52, 26 April 2020
YouTube search... ...Google search
- Guide
- Clustering
- A Beginner's Guide to Restricted Boltzmann Machines (RBMs) | Chris Nicholson - A.I. Wiki pathmind
- Deep Belief Network (DBN)
- Variational Autoencoder (VAE)
Useful for dimensionality reduction, classification, Regression, collaborative filtering, feature learning and topic modeling. RBMs are shallow, two-layer neural nets that constitute the building blocks of deep-belief networks. The biggest difference between Boltzmann Machine (BM) and RBMs is that RBMs are a better usable because they are more restricted. They don’t trigger-happily connect every neuron to every other neuron but only connect every different group of neurons to every other group, so no input neurons are directly connected to other input neurons and no hidden to hidden connections are made either. RBMs can be trained like FFNNs with a twist: instead of passing data forward and then back-propagating, you forward pass the data and then backward pass the data (back to the first layer). After that you train with forward-and-back-propagation. Smolensky, Paul. Information processing in dynamical systems: Foundations of harmony theory. No. CU-CS-321-86. COLORADO UNIV AT BOULDER DEPT OF COMPUTER SCIENCE, 1986.
