{"id":166046,"date":"2023-01-18T15:17:14","date_gmt":"2023-01-18T14:17:14","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=166046"},"modified":"2023-10-30T18:20:04","modified_gmt":"2023-10-30T17:20:04","slug":"perceptron-definition-and-use-cases","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/perceptron-definition-and-use-cases","title":{"rendered":"Perceptron: Concept, function, and applications"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"166046\" class=\"elementor elementor-166046\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c25b9de e-flex e-con-boxed e-con\" data-id=\"c25b9de\" 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-07bdc25 elementor-widget elementor-widget-text-editor\" data-id=\"07bdc25\" 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>A Perceptron is an artificial neuron, essential for Deep Learning neural networks. Discover its principle, its use, and its importance in Data Science.<\/b><\/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-f20a068 e-flex e-con-boxed e-con\" data-id=\"f20a068\" 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-e190894 elementor-widget elementor-widget-text-editor\" data-id=\"e190894\" 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;\">To understand what a Perceptron is, we must first understand the concept of <\/span><b>artificial neural networks<\/b><span style=\"font-weight: 400;\">. As you probably know, the human brain is made of billions of neurons.<\/span><\/p><p><span style=\"font-weight: 400;\">These neurons are<\/span><b> interconnected<\/b><span style=\"font-weight: 400;\"> nerve cells, and allow the processing and transmission of chemical and electrical signals. <\/span><b>Dendrites<\/b><span style=\"font-weight: 400;\"> are branches that receive information from other neurons. <\/span><b>The cell nuclei process the information<\/b><span style=\"font-weight: 400;\"> received from the dendrites. Finally, synapses serve as connections between neurons.<\/span><\/p><p><span style=\"font-weight: 400;\">Artificial neurons try to mimic the functioning of <\/span><b>brain neurons<\/b><span style=\"font-weight: 400;\">. It is a mathematical function based on a model of biological neurons. Each neuron receives data, weighs them, calculates their sum and produces a result <\/span><b>through a non-linear function<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">An artificial neural network consists of multiple artificial neurons. The <\/span><b>results of the computations are transmitted<\/b><span style=\"font-weight: 400;\"> from one neuron to another, and each neuron maintains an internal state called the activation signal. The neurons are linked together by connection links, through which information about the input data flows.<\/span><\/p><p><span style=\"font-weight: 400;\">In each neural network, we distinguish the input layer, the output layer, and different hidden layers. The data is transmitted from one layer to the other.<\/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-9b7bd30 elementor-widget elementor-widget-spacer\" data-id=\"9b7bd30\" data-element_type=\"widget\" data-widget_type=\"spacer.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-column .elementor-spacer-inner{height:var(--spacer-size)}.e-con{--container-widget-width:100%}.e-con-inner>.elementor-widget-spacer,.e-con>.elementor-widget-spacer{width:var(--container-widget-width,var(--spacer-size));--align-self:var(--container-widget-align-self,initial);--flex-shrink:0}.e-con-inner>.elementor-widget-spacer>.elementor-widget-container,.e-con-inner>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer,.e-con>.elementor-widget-spacer>.elementor-widget-container,.e-con>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer{height:100%}.e-con-inner>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer>.elementor-spacer-inner,.e-con>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer>.elementor-spacer-inner{height:var(--container-widget-height,var(--spacer-size))}<\/style>\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9d3b126 elementor-widget elementor-widget-heading\" data-id=\"9d3b126\" 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><h3 class=\"elementor-heading-title elementor-size-default\">What is the Perceptron?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cd41fee elementor-widget elementor-widget-text-editor\" data-id=\"cd41fee\" 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 Perceptron was invented in 1957 by <\/span><b>Frank Rosenblatt<\/b><span style=\"font-weight: 400;\"> at the Cornell Aeronautics Laboratory. Based on the first concepts of artificial neurons, he proposed the &#8220;<\/span><i><span style=\"font-weight: 400;\">Perceptron learning rule<\/span><\/i><span style=\"font-weight: 400;\">&#8220;.<\/span><\/p><p><span style=\"font-weight: 400;\">A Perceptron is an <\/span><b>artificial neuron<\/b><span style=\"font-weight: 400;\">, and thus a neural network unit. It performs computations to detect features or patterns in the input data. It is an algorithm for supervised learning of binary classifiers. It is this algorithm that <\/span><b>allows artificial neurons to learn<\/b><span style=\"font-weight: 400;\"> and <\/span><b>process features in a data set<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">The Perceptron plays an essential role in <\/span><span style=\"text-decoration: underline;\"><a href=\"https:\/\/datascientest.com\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\"><b>Machine Learning<\/b><\/a><\/span><span style=\"font-weight: 400;\"> projects. It is massively used to classify data, or as an algorithm to simplify or supervise the learning capabilities of binary classifiers.<\/span><\/p><p><span style=\"font-weight: 400;\">Recall that supervised learning consists in teaching an algorithm to <\/span><b>make predictions<\/b><span style=\"font-weight: 400;\">. To achieve this, the algorithm is fed with data that is already correctly labeled.<\/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-9cc4b8d elementor-widget__width-initial elementor-widget elementor-widget-video\" data-id=\"9cc4b8d\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=RNYT9bECfOo&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.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-video .elementor-widget-container{overflow:hidden;transform:translateZ(0)}.elementor-widget-video .elementor-wrapper{aspect-ratio:var(--video-aspect-ratio)}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{height:100%;width:100%;display:flex;border:none;background-color:#000}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-wrapper{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .elementor-open-inline .elementor-custom-embed-image-overlay{position:absolute;top:0;right:0;bottom:0;left:0;background-size:cover;background-position:50%}.elementor-widget-video .elementor-custom-embed-image-overlay{cursor:pointer;text-align:center}.elementor-widget-video .elementor-custom-embed-image-overlay:hover .elementor-custom-embed-play i{opacity:1}.elementor-widget-video .elementor-custom-embed-image-overlay img{display:block;width:100%;aspect-ratio:var(--video-aspect-ratio);-o-object-fit:cover;object-fit:cover;-o-object-position:center center;object-position:center center}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-custom-embed-image-overlay{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-custom-embed-image-overlay img{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .e-hosted-video .elementor-video{-o-object-fit:cover;object-fit:cover}.e-con-inner>.elementor-widget-video,.e-con>.elementor-widget-video{width:var(--container-widget-width);--flex-grow:var(--container-widget-flex-grow)}<\/style>\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6bf633b elementor-widget elementor-widget-spacer\" data-id=\"6bf633b\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\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-d9bd625 e-flex e-con-boxed e-con\" data-id=\"d9bd625\" 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-7928de7 elementor-widget elementor-widget-heading\" data-id=\"7928de7\" 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\">What\u2019s the Perceptron Learning Rule? <\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-af64a3e elementor-widget elementor-widget-text-editor\" data-id=\"af64a3e\" 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;\">According to the <\/span><b>Perceptron Learning Rule<\/b><span style=\"font-weight: 400;\">, the algorithm automatically learns the optimal weight coefficients. The characteristics of the input data are <\/span><b>multiplied<\/b><span style=\"font-weight: 400;\"> by these weights to determine whether a neuron &#8220;lights up&#8221; or not.<\/span><\/p><p><span style=\"font-weight: 400;\">The Perceptron receives <\/span><b>multiple<\/b><span style=\"font-weight: 400;\"> input signals. If the sum of the signals exceeds a certain threshold, a signal is produced or, conversely, no output is produced.<\/span><\/p><p><span style=\"font-weight: 400;\">In the context of the supervised learning method of <\/span><b>Machine Learning<\/b><span style=\"font-weight: 400;\">, this is what makes it possible to predict the category of a data sample. It is therefore an essential element.<\/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-0467a52 elementor-widget elementor-widget-image\" data-id=\"0467a52\" 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=\"800\" height=\"302\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/03\/perceptron-formule.png\" class=\"attachment-large size-large wp-image-55333\" alt=\"formule perceptron\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/03\/perceptron-formule.png 817w, https:\/\/datascientest.com\/en\/files\/2021\/03\/perceptron-formule-300x113.png 300w, https:\/\/datascientest.com\/en\/files\/2021\/03\/perceptron-formule-768x290.png 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\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-9a609b1 elementor-widget elementor-widget-spacer\" data-id=\"9a609b1\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\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-69f1d3d e-flex e-con-boxed e-con\" data-id=\"69f1d3d\" 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-5a8f625 elementor-widget elementor-widget-heading\" data-id=\"5a8f625\" 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\">The function of the Perceptron, how to interpret the result?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3ce2da7 elementor-widget elementor-widget-text-editor\" data-id=\"3ce2da7\" 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;\">In reality, <strong>the Perceptron is a mathematical function<\/strong>. The input data (x) is multiplied by the <\/span><b>weight coefficients<\/b><span style=\"font-weight: 400;\"> (w). The result is a value.<\/span><\/p><p><span style=\"font-weight: 400;\">This value can be positive or negative. The artificial neuron is activated if the value is <\/span><b>positive<\/b><span style=\"font-weight: 400;\">. It is therefore activated only if the calculated weight of the input data exceeds a certain threshold.<\/span><\/p><p><span style=\"font-weight: 400;\">The predicted result is <\/span><b>compared<\/b><span style=\"font-weight: 400;\"> with the known result. If there is a difference, the error is back propagated in order to <\/span><b>adjust the weights<\/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<div class=\"elementor-element elementor-element-1c3b5d6 elementor-widget elementor-widget-spacer\" data-id=\"1c3b5d6\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\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-b23de1b e-flex e-con-boxed e-con\" data-id=\"b23de1b\" 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-6e54431 elementor-widget elementor-widget-heading\" data-id=\"6e54431\" 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\">Single layer vs. multi-layer perceptron<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f0dfb85 elementor-widget elementor-widget-text-editor\" data-id=\"f0dfb85\" 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;\">There are <\/span><b>two types of Perceptron:<\/b><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A <\/span><b>single layer Perceptron<\/b><span style=\"font-weight: 400;\"> can learn only separable linear functions.<\/span><\/li><\/ul><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A <\/span><b>multi-layer Perceptron<\/b><span style=\"font-weight: 400;\">, also known as a feed-forward neural network, overcomes this limitation and offers superior computational power. It is also possible to combine several Perceptrons to create a <\/span><b>powerful mechanism<\/b><span style=\"font-weight: 400;\">.<\/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-55c5b4d elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"55c5b4d\" 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-md\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\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\">All about the Perceptron and neural networks<\/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<div class=\"elementor-element elementor-element-00d194a elementor-widget elementor-widget-spacer\" data-id=\"00d194a\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\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-7d21572 e-flex e-con-boxed e-con\" data-id=\"7d21572\" 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-ede5301 elementor-widget elementor-widget-heading\" data-id=\"ede5301\" 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\">Perceptron and Neural Networks\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5fa40d9 elementor-widget elementor-widget-text-editor\" data-id=\"5fa40d9\" 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;\">In short, a neural network is a set of <\/span><b>interconnected Perceptrons<\/b><span style=\"font-weight: 400;\">. Its operation is based on multiplication operations between two important components: <\/span><b>the input<\/b><span style=\"font-weight: 400;\"> and <\/span><b>the weight<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">The sum of this multiplication is passed to an <\/span><b>activation function<\/b><span style=\"font-weight: 400;\">, determining a binary value of 0 or 1. This is what allows the data to be <\/span><b>classified<\/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<div class=\"elementor-element elementor-element-9edbb7b elementor-widget elementor-widget-spacer\" data-id=\"9edbb7b\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\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-897f06c e-flex e-con-boxed e-con\" data-id=\"897f06c\" 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-8e4dd63 elementor-widget elementor-widget-heading\" data-id=\"8e4dd63\" 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\">How to learn how to use the Perceptron?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-75d2eae elementor-widget elementor-widget-text-editor\" data-id=\"75d2eae\" 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;\">To learn how to master the Perceptron, and understand everything about Machine Learning, Deep Learning and Neural Networks, you can opt for <\/span><span style=\"text-decoration: underline;\"><strong><a href=\"https:\/\/datascientest.com\/en\/data-courses\">DataScientest&#8217;s training courses<\/a><\/strong><\/span><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Machine Learning, and the different algorithms and methodologies, are at the heart of our <\/span><span style=\"text-decoration: underline;\"><strong><a href=\"https:\/\/datascientest.com\/en\/data-analyst-course\">Data Analyst <\/a><\/strong><\/span><span style=\"font-weight: 400;\">and Data Scientist trainings. We also offer a <\/span><strong><span style=\"text-decoration: underline;\"><a href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-course\">Machine Learning Engineer course<\/a><\/span><\/strong><span style=\"font-weight: 400;\"> to learn how to put models into production.<\/span><\/p><p><span style=\"font-weight: 400;\">These different courses will enable you to acquire all the <\/span><b>skills<\/b><span style=\"font-weight: 400;\"> required to work as a data analyst or data scientist. In addition to Machine Learning, you will also learn to master Python programming, databases and Data Visualization.<\/span><\/p><p><span style=\"font-weight: 400;\">All of our courses adopt an innovative &#8220;B<\/span><b>lended learning<\/b><span style=\"font-weight: 400;\">&#8221; approach, halfway between classroom and distance learning. They can be done in Part-time education, or in bootcamp in just a few months.<\/span><\/p><p><span style=\"font-weight: 400;\">Learners receive a diploma <\/span><b>certified by the Sorbonne University<\/b><span style=\"font-weight: 400;\">. Among our alumni,<\/span><b> 93%<\/b> <b>found a job immediately<\/b><span style=\"font-weight: 400;\"> after graduation. Don&#8217;t wait any longer, and discover our training courses!<\/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-df7537d elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"df7537d\" 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-md\" 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 DataScientest's 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-b5591a4 e-flex e-con-boxed e-con\" data-id=\"b5591a4\" 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-bf15b7d elementor-widget elementor-widget-html\" data-id=\"bf15b7d\" 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    \"@type\": \"Question\",\r\n    \"name\": \"What is the Perceptron?\u200b\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"A Perceptron is an artificial neuron, and thus a neural network unit. 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As you probably know, the human brain is made of billions of neurons. These neurons are [&hellip;]<\/p>\n","protected":false},"author":63,"featured_media":62649,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"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>Perceptron: Concept, function, and applications<\/title>\n<meta name=\"description\" content=\"A Perceptron is an artificial neuron, essential for Deep Learning neural networks. 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