Restricted Boltzmann Machine (RBM) - Revision history

BPeat at 01:06, 9 August 2020

2020-08-09T01:06:52Z

BPeat at 01:05, 9 August 2020

2020-08-09T01:05:22Z

BPeat at 12:29, 12 July 2020

2020-07-12T12:29:16Z

BPeat at 20:52, 26 April 2020

2020-04-26T20:52:42Z

BPeat at 20:46, 26 April 2020

2020-04-26T20:46:15Z

BPeat at 06:42, 13 July 2019

2019-07-13T06:42:20Z

BPeat at 00:42, 23 April 2019

2019-04-23T00:42:23Z

BPeat at 14:17, 3 February 2019

2019-02-03T14:17:59Z

BPeat at 03:02, 13 May 2018

2018-05-13T03:02:05Z

BPeat at 16:40, 11 May 2018

2018-05-11T16:40:21Z

@@ Line 19: / Line 19: @@
 <youtube>p4Vh_zMw-HQ</youtube>
-<youtube>puux7KZQfsE</youtube>
+<youtube>Fkw0_aAtwIw</youtube>
 <youtube>it_PXVIMyWg</youtube>
 <youtube>puux7KZQfsE</youtube>

@@ Line 21: / Line 21: @@
 <youtube>puux7KZQfsE</youtube>
 <youtube>it_PXVIMyWg</youtube>
-<youtube>JfpP9CY1EFo</youtube>
+<youtube>puux7KZQfsE</youtube>

@@ Line 14: / Line 14: @@
 * [[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.
+'The Model T of neural networks" - 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.
 http://www.asimovinstitute.org/wp-content/uploads/2016/09/rbm.png

@@ Line 10: / Line 10: @@
 * [http://deeplearning4j.org/restrictedboltzmannmachine.html Guide]
 * [[Clustering]]
-* [https://pathmind.com/wiki/restricted-boltzmann-machine A Beginner's Guide to Restricted Boltzmann Machines (RBMs) | Chris Nicholson - A.I. Wiki pathmind]
+* [http://pathmind.com/wiki/restricted-boltzmann-machine A Beginner's Guide to Restricted Boltzmann Machines (RBMs) | Chris Nicholson - A.I. Wiki pathmind]
 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.

@@ Line 10: / Line 10: @@
 * [http://deeplearning4j.org/restrictedboltzmannmachine.html Guide]
 * [[Clustering]]
 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.

@@ Line 8: / Line 8: @@
 [http://www.google.com/search?q=Restricted+Boltzmann+Machines+RBM+machine+learning+ML+artificial+intelligence ...Google search]
-* [https://deeplearning4j.org/restrictedboltzmannmachine.html Guide]
+* [http://deeplearning4j.org/restrictedboltzmannmachine.html Guide]
 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.

← Older revision		Revision as of 14:17, 3 February 2019
Line 1:		Line 1:
−	[http://www.youtube.com/results?search_query=Restricted+Boltzmann+Machines YouTube search...]	+	{{#seo:
		+	\|title=PRIMO.ai
		+	\|titlemode=append
		+	\|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
		+	\|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
		+	}}
		+	[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]

	* [https://deeplearning4j.org/restrictedboltzmannmachine.html Guide]		* [https://deeplearning4j.org/restrictedboltzmannmachine.html Guide]

← Older revision		Revision as of 03:02, 13 May 2018
Line 4:		Line 4:

	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.		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.
		+
		+	http://www.asimovinstitute.org/wp-content/uploads/2016/09/rbm.png

	<youtube>p4Vh_zMw-HQ</youtube>		<youtube>p4Vh_zMw-HQ</youtube>