Restricted Boltzmann Machines
Offered By: YouTube
Course Description
Overview
Restricted Boltzmann Machines (RBM) are a particular type of Gibbsian network or graphical model that is distinguished by having a layered structure with a hidden and a visible layer and by the fact that units within a layer are not connected to each other, which makes the units statistically independent given the other layer. RBMs can have many units and are often used to model high dimensional data, such as images.
Syllabus
RBM 1.1-1.3 - Restricted Boltzmann Machines (32 min).
RBM 1.4-1.7 - Restricted Boltzmann Machines (25 min).
Taught by
Prof. Laurenz Wiskott
Related Courses
Neural Networks for Machine LearningUniversity of Toronto via Coursera Good Brain, Bad Brain: Basics
University of Birmingham via FutureLearn Statistical Learning with R
Stanford University via edX Machine Learning 1—Supervised Learning
Brown University via Udacity Fundamentals of Neuroscience, Part 2: Neurons and Networks
Harvard University via edX