A Boltzmann machine (also called Sherrington–Kirkpatrick model with external field or stochastic Ising–Lenz–Little model) is a stochastic spin-glass model with an external field, i.e., a Sherrington–Kirkpatrick model, that is a stochastic Ising Model. It is a statistical physics technique applied in the context of cognitive science. It is also classified as Markov random field. Boltzmann machines are theoretically intriguing because of the locality and Hebbian nature of their training algorithm (being trained by Hebb's rule), and because of their parallelism and the resemblance of their dynamics to simple physical processes. Boltzmann machines with unconstrained connectivity have not been proven useful for practical problems in machine learning or inference, but if the connectivity is properly constrained, the learning can be made efficient enough to be useful for practical problems. They are named after the Boltzmann distribution in statistical mechanics, which is used in their sampling function. They were heavily popularized and promoted by Geoffrey Hinton, Terry Sejnowski and Yann LeCun in cognitive sciences communities and in machine learning. As a more general class within machine learning these models are called "energy based models" (EBM), because Hamiltonian of spin glasses are used as a starting point to define the learning task. (Wikipedia).
Lecture 12A : The Boltzmann Machine learning algorithm
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 12A : The Boltzmann Machine learning algorithm
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Lecture 12/16 : Restricted Boltzmann machines (RBMs)
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 12A The Boltzmann Machine learning algorithm 12B More efficient ways to get the statistics 12C Restricted Boltzmann Machines 12D An example of Contrastive Divergence Learning 12E RBMs for collaborative filtering
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Lecture 12.1 — Boltzmann machine learning [Neural Networks for Machine Learning]
For cool updates on AI research, follow me at https://twitter.com/iamvriad. Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. Link to the course (login required): https://class.coursera.org/neuralnets-
From playlist [Coursera] Neural Networks for Machine Learning — Geoffrey Hinton
The Mandelbrot set is a churning machine
Its job is to fling off the red pixels and hang onto the green ones. Audio by @Dorfmandesign
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http://www.mekanizmalar.com/napier_deltic_engine.html Working principle of a Napier Deltic Engine.
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Combination of gear drive and slider-crank mechanism. Inventor files of this video: http://www.mediafire.com/file/g440tz6eu1brn9c/HandPunch1Inv.zip/file
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Lecture 14.1 — Learning layers of features by stacking RBMs [Neural Networks for Machine Learning]
Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. Link to the course (login required): https://class.coursera.org/neuralnets-2012-001
From playlist [Coursera] Neural Networks for Machine Learning — Geoffrey Hinton
Lecture 14A : Learning layers of features by stacking RBMs
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 14A : Learning layers of features by stacking RBMs
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Lecture 14/16 : Deep neural nets with generative pre-training
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 14A Learning layers of features by stacking RBMs 14B Discriminative fine-tuning for DBNs 14C What happens during discriminative fine-tuning? 14D Modeling real-valued data with an RBM 14E RBMs are Infinite Sigmoid Beli
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Lecture 16A : Learning a joint model of images and captions
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 16A : Learning a joint model of images and captions
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Lecture 16.1 — Learning a joint model of images and captions [Neural Networks for Machine Learning]
Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. Link to the course (login required): https://class.coursera.org/neuralnets-2012-001
From playlist [Coursera] Neural Networks for Machine Learning — Geoffrey Hinton
Deep Learning Lecture 10.3 - Restricted Boltzmann Machines
Restricted Boltzmann Machines: - Architecture - Energy - Gibbs Sampling and Contrastive Divergence
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AWESOME simple Vacuum gun - Old cannon gun - Coil gun!
In this video i show vacuum gun with hoover explaining how it works, old cannon gun with liquid gas explaining Newton' s third law and electromagnetic gun (coil gun)!
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Restricted Boltzmann Machine | Neural Network Tutorial | Deep Learning Tutorial | Edureka
** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** This Edureka video on "Restricted Boltzmann Machine" will provide you with a detailed and comprehensive knowledge of Restricted Boltzmann Machines, also known as RBM. You will also
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