{"id":196373,"date":"2025-07-11T06:26:00","date_gmt":"2025-07-11T05:26:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=196373"},"modified":"2025-07-06T21:27:16","modified_gmt":"2025-07-06T20:27:16","slug":"all-about-restricted-boltzmann-machines","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/all-about-restricted-boltzmann-machines","title":{"rendered":"What are Restricted Boltzmann Machines (RBM)? What are they used for?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"196373\" class=\"elementor elementor-196373\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7ab32d4 e-flex e-con-boxed e-con\" data-id=\"7ab32d4\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-adeb28b elementor-widget elementor-widget-text-editor\" data-id=\"adeb28b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><b>Restricted Boltzmann Machines (RBM) are a form of artificial neural network crafted for unsupervised learning, enabling the learning of a probability distribution from a set of input data.<\/b><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3b2be8a elementor-widget elementor-widget-text-editor\" data-id=\"3b2be8a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Initially developed by Geoffrey Hinton and Terry Sejnowski in 1985 and gaining popularity in the 2000s, RBMs excel in <\/span><b>dimensionality reduction<\/b><span style=\"font-weight: 400;\">, <\/span><b>feature extraction<\/b><span style=\"font-weight: 400;\">, and <\/span><b>imputing missing data<\/b><span style=\"font-weight: 400;\">. They frequently serve as foundational components for deeper architectures like <\/span><b>Deep Belief Networks (DBN)<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6434293 e-flex e-con-boxed e-con\" data-id=\"6434293\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b718099 elementor-widget elementor-widget-heading\" data-id=\"b718099\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-xl\">What is the origin of RBMs?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7ff3e43 elementor-widget elementor-widget-text-editor\" data-id=\"7ff3e43\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">RBMs are a simplified form of <\/span><b>Boltzmann Machines (BM)<\/b><span style=\"font-weight: 400;\">, which are energy-based neural networks with fully interconnected neurons. In contrast, RBMs restrict connections between neurons within the same layer, simplifying calculations and training. This restriction empowers RBMs to capture meaningful latent representations in fields such as computer vision, <\/span><span style=\"font-weight: 400;\">natural language processing<\/span><span style=\"font-weight: 400;\">, and content recommendation.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bc39eb0 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"bc39eb0\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Learn how to use RBMs<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f480fc6 e-flex e-con-boxed e-con\" data-id=\"f480fc6\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a6d05f6 elementor-widget elementor-widget-heading\" data-id=\"a6d05f6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">How do RBMs work?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f41228d elementor-widget elementor-widget-text-editor\" data-id=\"f41228d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><b>Restricted Boltzmann Machines (RBM)<\/b><span style=\"font-weight: 400;\"> operate through a distinct architecture consisting of two neuron layers: a visible layer representing the <\/span><b>input data<\/b><span style=\"font-weight: 400;\"> and a <\/span><b>hidden layer<\/b><span style=\"font-weight: 400;\"> that extracts key features. Unlike conventional neural networks, they lack an output layer, focusing instead on modeling a <\/span><b>data probability distribution<\/b><span style=\"font-weight: 400;\">. Learning is achieved by adjusting the weights between these two layers, without any intra-layer connections.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ff63b97 elementor-widget elementor-widget-heading\" data-id=\"ff63b97\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">1. Architecture of an RBM<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9b7108e elementor-widget elementor-widget-text-editor\" data-id=\"9b7108e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">RBMs are structured as symmetric bipartite graphs where each neuron in the <\/span><b>visible layer<\/b><span style=\"font-weight: 400;\"> connects with every neuron in the hidden layer, lacking any roadmaps for <\/span><span style=\"font-weight: 400;\">intra-layer connections<\/span><span style=\"font-weight: 400;\">. Each connection carries a weight, updated during training.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c7b28cb elementor-widget elementor-widget-heading\" data-id=\"c7b28cb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">2. Learning Phase<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ec2b4fa elementor-widget elementor-widget-text-editor\" data-id=\"ec2b4fa\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">During learning, a technique known as <\/span><b>Contrastive Divergence (CD-k)<\/b><span style=\"font-weight: 400;\"> is employed for weight updates. The process begins by introducing an input vector to the visible layer, which relays information to the hidden layer via a <\/span><b>sigmoid activation function<\/b><span style=\"font-weight: 400;\">. A new sample is subsequently generated from the hidden layer to reconstruct an estimate of the initial input. The variance between this reconstruction and the original input guides the adjustment of model weights. This iterative process continues until the <\/span><b>weight updates<\/b><span style=\"font-weight: 400;\"> become negligible.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2691237 elementor-widget elementor-widget-image\" data-id=\"2691237\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1000\" height=\"571\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/Restricted-Boltzmann-Machine-DataScientest-1.webp\" class=\"attachment-full size-full wp-image-196377\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/Restricted-Boltzmann-Machine-DataScientest-1.webp 1000w, https:\/\/datascientest.com\/en\/files\/2025\/05\/Restricted-Boltzmann-Machine-DataScientest-1-300x171.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/05\/Restricted-Boltzmann-Machine-DataScientest-1-768x439.webp 768w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8bb1c2a elementor-widget elementor-widget-heading\" data-id=\"8bb1c2a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">3. Energy Function and Probability Distribution<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-10d82ab elementor-widget elementor-widget-text-editor\" data-id=\"10d82ab\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">RBMs utilize an energy function to calculate the probability of a specific state. The joint probability of the <\/span><b>visible and hidden layers<\/b><span style=\"font-weight: 400;\"> follows the Boltzmann distribution.<\/span><\/p><p><span style=\"font-weight: 400;\">The Boltzmann distribution formula specifies the probability <\/span><b><i>\u03a1(\u0395)<\/i><\/b><span style=\"font-weight: 400;\"> of a particle residing in an energy state <\/span><b><i>\u0395<\/i><\/b><span style=\"font-weight: 400;\"> at a temperature <\/span><b><i>\u03a4<\/i><\/b><span style=\"font-weight: 400;\">. It is articulated as follows:<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-75670b4 elementor-widget elementor-widget-image\" data-id=\"75670b4\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"308\" height=\"137\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/distribution-de-Boltzmann.png\" class=\"attachment-full size-full wp-image-196381\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/distribution-de-Boltzmann.png 308w, https:\/\/datascientest.com\/en\/files\/2025\/05\/distribution-de-Boltzmann-300x133.png 300w\" sizes=\"(max-width: 308px) 100vw, 308px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-076d245 elementor-widget elementor-widget-text-editor\" data-id=\"076d245\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Where:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b><i>P(E)<\/i><\/b><span style=\"font-weight: 400;\"> represents the state probability of energy <\/span><b><i>E<\/i><\/b><span style=\"font-weight: 400;\">,<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b><i>E<\/i><\/b><span style=\"font-weight: 400;\"> is the state&#8217;s energy,<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b><i>k<\/i><\/b><b><i>B<\/i><\/b><b><i>\u200b<\/i><\/b><span style=\"font-weight: 400;\"> is the Boltzmann constant,<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b><i>T<\/i><\/b><span style=\"font-weight: 400;\"> denotes the temperature in kelvins,<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b><i>Z<\/i><\/b><span style=\"font-weight: 400;\"> represents the partition function<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8bfaf0b elementor-widget elementor-widget-text-editor\" data-id=\"8bfaf0b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Thus, a state is more probable when its energy is lower.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dac6f5e elementor-widget elementor-widget-heading\" data-id=\"dac6f5e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">4. Weight Update<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-74af00f elementor-widget elementor-widget-text-editor\" data-id=\"74af00f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The neuron connection weights are updated by minimizing the reconstruction error. The update rule is expressed as:<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ff4afee elementor-widget elementor-widget-image\" data-id=\"ff4afee\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"543\" height=\"120\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/equation-de-mise-a-jour.png\" class=\"attachment-full size-full wp-image-196382\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/equation-de-mise-a-jour.png 543w, https:\/\/datascientest.com\/en\/files\/2025\/05\/equation-de-mise-a-jour-300x66.png 300w\" sizes=\"(max-width: 543px) 100vw, 543px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5adbb51 elementor-widget elementor-widget-text-editor\" data-id=\"5adbb51\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">where <\/span><b>\u0394\u03c9<\/b><span style=\"font-weight: 400;\"> represents the weight adjustment, <\/span><b><i>\u03b7<\/i><\/b><span style=\"font-weight: 400;\"> is the learning rate, <\/span><b><i>v<\/i><\/b><span style=\"font-weight: 400;\"> and <\/span><b><i>h<\/i><\/b><span style=\"font-weight: 400;\"> are the visible and hidden neuron activations, respectively, and <\/span><b><i>v\u0302<\/i><\/b><span style=\"font-weight: 400;\"> and\u00a0<\/span><b><i>\u0125<\/i><\/b><span style=\"font-weight: 400;\"> are the reconstructed activations. This adjustment refines the weights to diminish the error between actual and reconstructed activations.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5a1e7ef elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"5a1e7ef\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Follow a course in RBM<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e48ad70 e-flex e-con-boxed e-con\" data-id=\"e48ad70\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-aa84784 elementor-widget elementor-widget-heading\" data-id=\"aa84784\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">What are the advantages and disadvantages of RBMs?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a61785d elementor-widget elementor-widget-heading\" data-id=\"a61785d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">1. Advantages<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-80fe031 elementor-widget elementor-widget-text-editor\" data-id=\"80fe031\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">RBMs offer several benefits. Notably:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Unsupervised Learning<\/b><span style=\"font-weight: 400;\">: Their operation through unsupervised learning makes RBMs particularly adept at feature extraction from raw data.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Ability to model complex and high-dimensional data<\/b><span style=\"font-weight: 400;\">: They excel at modeling intricate, high-dimensional distributions.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Serve as foundational blocks in deep architectures (DBN)<\/b><span style=\"font-weight: 400;\">: They are integral to developing deeper architectures like Deep Belief Networks.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f4d347a elementor-widget elementor-widget-heading\" data-id=\"f4d347a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">2. Disadvantages<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c904197 elementor-widget elementor-widget-text-editor\" data-id=\"c904197\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Challenge in identifying optimal hyperparameters<\/b><span style=\"font-weight: 400;\">: The learning rate must be finely tuned; too high a rate might cause oscillations and hinder convergence, while too low a rate significantly slows learning. Moreover, the number of hidden neurons greatly affects the model&#8217;s capacity to capture relevant representations. An insufficient number limits the richness of extracted features, whereas too many risk overfitting.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Lengthy training process for large datasets<\/b><span style=\"font-weight: 400;\">: A substantial iteration count necessary for apt weight adjustments poses constraints, especially with large datasets needing numerous computational operations for each weight update.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bbd3b70 elementor-widget elementor-widget-image\" data-id=\"bbd3b70\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1000\" height=\"571\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/Restricted-Boltzmann-Machine-DataScientest-2.webp\" class=\"attachment-full size-full wp-image-196376\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/Restricted-Boltzmann-Machine-DataScientest-2.webp 1000w, https:\/\/datascientest.com\/en\/files\/2025\/05\/Restricted-Boltzmann-Machine-DataScientest-2-300x171.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/05\/Restricted-Boltzmann-Machine-DataScientest-2-768x439.webp 768w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1386232 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"1386232\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Training in the use of RBM<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-fd21b46 e-flex e-con-boxed e-con\" data-id=\"fd21b46\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a9a8a3e elementor-widget elementor-widget-heading\" data-id=\"a9a8a3e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">How are RBMs used?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fce85d5 elementor-widget elementor-widget-text-editor\" data-id=\"fce85d5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Restricted Boltzmann Machines find applications across diverse fields:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Collaborative filtering<\/b><span style=\"font-weight: 400;\">: Applied in recommendation systems to forecast user preferences.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Computer vision<\/b><span style=\"font-weight: 400;\">: Employed in object recognition, image restoration, and noise reduction.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Natural language processing<\/b><span style=\"font-weight: 400;\">: Utilized for language modeling, text categorization, and sentiment analysis.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Bioinformatics<\/b><span style=\"font-weight: 400;\">: For protein structure prediction and gene expression analysis.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Finance<\/b><span style=\"font-weight: 400;\">: Used in stock price forecasting, risk management, and fraud detection.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Anomaly detection<\/b><span style=\"font-weight: 400;\">: Applied in fraud transaction identification, network observation, and medical diagnostics.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a5ca683 elementor-widget elementor-widget-text-editor\" data-id=\"a5ca683\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">RBMs&#8217; applicability spans various domains: in <\/span><b>recommendation systems<\/b><span style=\"font-weight: 400;\">, they refine collaborative filtering to predict user preferences; in computer vision, they assist in object detection, <\/span><b>image noise reduction<\/b><span style=\"font-weight: 400;\">, and <\/span><b>visual data reconstruction<\/b><span style=\"font-weight: 400;\">; for natural language processing, they contribute to <\/span><a href=\"https:\/\/datascientest.com\/en\/large-language-models-llm-everything-you-need-to-know\"><span style=\"font-weight: 400;\">language modeling<\/span><\/a><span style=\"font-weight: 400;\">, sentiment analysis, and <\/span><b>text classification<\/b><span style=\"font-weight: 400;\">. They also play roles in <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-bioinformatics\"><span style=\"font-weight: 400;\">bioinformatics<\/span><\/a><span style=\"font-weight: 400;\">, notably for gene expression analysis and protein structure prediction, as well as in finance for stock predictions and fraud prevention. Lastly, they are deployed in <\/span><span style=\"font-weight: 400;\">cybersecurity<\/span><span style=\"font-weight: 400;\"> and <\/span><span style=\"font-weight: 400;\">medical diagnostics<\/span><span style=\"font-weight: 400;\"> to identify anomalies and detect atypical behaviors.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-a87f0fc e-flex e-con-boxed e-con\" data-id=\"a87f0fc\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-263ae79 elementor-widget elementor-widget-heading\" data-id=\"263ae79\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">Conclusion<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bc33ae6 elementor-widget elementor-widget-text-editor\" data-id=\"bc33ae6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><b>Restricted Boltzmann Machines<\/b><span style=\"font-weight: 400;\"> are formidable tools for <\/span><span style=\"font-weight: 400;\">unsupervised learning<\/span><span style=\"font-weight: 400;\"> and feature extraction. Their capacity to learn meaningful representations renders them invaluable for a myriad of artificial intelligence applications. Despite training and parameterization challenges, they remain crucial in creating advanced models like <\/span><b>Deep Belief Networks<\/b><span style=\"font-weight: 400;\"> and other <\/span><a href=\"https:\/\/datascientest.com\/en\/deep-neural-network-what-is-it-and-how-is-it-working\"><span style=\"font-weight: 400;\">deep neural architectures<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-11db9de elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"11db9de\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover our training courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-95cde90 e-flex e-con-boxed e-con\" data-id=\"95cde90\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-11bc7b3 elementor-widget elementor-widget-html\" data-id=\"11bc7b3\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"What is the origin of RBMs?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"RBMs are simplified Boltzmann Machines that restrict connections within layers, enabling easier training and capturing meaningful latent representations.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"How do RBMs work?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"RBMs have a visible layer for input and a hidden layer for features, with weights updated by Contrastive Divergence to minimize reconstruction error and model data probability.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"What are the advantages and disadvantages of RBMs?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Advantages include unsupervised learning, modeling complex data, and use in deep architectures. 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[&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":196375,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2427],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>What are Restricted Boltzmann Machines (RBM)? What are they used for?<\/title>\n<meta name=\"description\" content=\"Discover Restricted Boltzmann Machines (RBM), a powerful model for unsupervised learning. 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