{"id":166044,"date":"2023-01-19T09:48:00","date_gmt":"2023-01-19T08:48:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=166044"},"modified":"2023-10-30T18:20:29","modified_gmt":"2023-10-30T17:20:29","slug":"recurrent-neural-network-what-is-it","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/recurrent-neural-network-what-is-it","title":{"rendered":"Recurrent Neural Networks: The complete Guide"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"166044\" class=\"elementor elementor-166044\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e5445f8 e-flex e-con-boxed e-con\" data-id=\"e5445f8\" 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-d338f90 elementor-widget elementor-widget-text-editor\" data-id=\"d338f90\" 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><strong>If you are a follower of our blog, you already know what a neural network is (if not, feel free to read this article first) but what does the adjective recurrent bring to this model? In this article, we will see how recurrent neural networks, called RNN, have become a classic model in <span style=\"text-decoration: underline;\"><a href=\"https:\/\/datascientest.com\/en\/all-about-deep-learning\">deep learning<\/a><\/span>.<\/strong><\/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-3e135bf elementor-widget elementor-widget-spacer\" data-id=\"3e135bf\" 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\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-bd9386f e-flex e-con-boxed e-con\" data-id=\"bd9386f\" 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-46245fd elementor-widget elementor-widget-heading\" data-id=\"46245fd\" 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\">How to set up this neural network in situations ?\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-14c6c6b elementor-widget elementor-widget-text-editor\" data-id=\"14c6c6b\" 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;\">Before explaining a <\/span><b>RNN<\/b><span style=\"font-weight: 400;\">, let&#8217;s focus on a bullet. Indeed, it is common in <\/span><span style=\"text-decoration: underline;\"><strong><a href=\"https:\/\/datascientest.com\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\">Machine Learning<\/a><\/strong><\/span><span style=\"font-weight: 400;\"> to want to predict the trajectory of a movable object. As shown in figure 1, from the starting point, the ball can take <\/span><b>all sorts of directions<\/b><span style=\"font-weight: 400;\">. How can we know what the next movement of the ball is?<\/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-b343117 elementor-widget elementor-widget-image\" data-id=\"b343117\" 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=\"656\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1-1024x840.png\" class=\"attachment-large size-large wp-image-80700\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1-1024x840.png 1024w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1-300x246.png 300w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1-768x630.png 768w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1.png 1349w\" 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-5c6649b elementor-widget elementor-widget-text-editor\" data-id=\"5c6649b\" 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;\">However, if we take <\/span><b>figure 2<\/b><span style=\"font-weight: 400;\">, it is obvious to say that the ball will continue to go <\/span><b>towards the right<\/b><span style=\"font-weight: 400;\">, thanks to the past trajectories, which translate a movement towards the right.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">So far, everything seems logical.<\/span><\/p><p><span style=\"font-weight: 400;\">Contrary to figure 1, we have more training data, from which we are better able to decide on the movement of the ball.<\/span><\/p><p><span style=\"font-weight: 400;\">Thus, we just need to give our <\/span><b>neural network<\/b><span style=\"font-weight: 400;\"> the old ball movements and our study is over. However, how do we choose the number of input neurons? A trajectory can be split in any way we want. Whether it is 10 or 100.<\/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-72f15e8 elementor-widget elementor-widget-image\" data-id=\"72f15e8\" 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=\"800\" height=\"243\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1-copie-1024x311.png\" class=\"attachment-large size-large wp-image-80699\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1-copie-1024x311.png 1024w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1-copie-300x91.png 300w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1-copie-768x234.png 768w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1-copie-1536x467.png 1536w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1-copie-2048x623.png 2048w\" 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-2a39547 elementor-widget elementor-widget-text-editor\" data-id=\"2a39547\" 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>The white ball represents the current position of the ball and the light blue balls represent the\u00a0<b>old trajectories<\/b>\u00a0of the white ball, so we guess that the ball is heading to the right.\u00a0<\/p><p>Let&#8217;s ignore this detail for now, and fix the size of our input samples. Let&#8217;s look at\u00a0<b>Figure 3<\/b>, where will the ball go?<\/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-c6c4282 elementor-widget elementor-widget-image\" data-id=\"c6c4282\" 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=\"800\" height=\"484\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1-copie-2-1024x619.png\" class=\"attachment-large size-large wp-image-80698\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1-copie-2-1024x619.png 1024w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1-copie-2-300x181.png 300w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1-copie-2-768x464.png 768w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere_Plan-de-travail-1-copie-2.png 1349w\" 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-a1e140f elementor-widget elementor-widget-text-editor\" data-id=\"a1e140f\" 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;\">Admittedly, the motion of the ball is <\/span><b>more complex <\/b><span style=\"font-weight: 400;\">than in figure 1, but we can still tell that the next move of the ball will be <\/span><b>upwards<\/b><span style=\"font-weight: 400;\">. Will the model make the same prediction? Unfortunately not, because the neural network does not think like we do! The model, unlike us, does not take into account the link between the inputs. The <\/span><b>inputs are not independent<\/b><span style=\"font-weight: 400;\"> of each other, so we must preserve this link between them when we train our neural network.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">We need to overcome <\/span><b>2 problems<\/b><span style=\"font-weight: 400;\">:\u00a0<\/span><\/p><ul><li aria-level=\"1\"><b>The size<\/b><span style=\"font-weight: 400;\"> of our input samples is not fixed.<\/span><\/li><li aria-level=\"1\"><span style=\"font-weight: 400;\">The input data are <\/span><b>not linked<\/b><span style=\"font-weight: 400;\"> together.\u00a0<\/span><\/li><\/ul><div>\u00a0<\/div><p><span style=\"font-weight: 400;\">Let&#8217;s go back to our ball trajectory study. We will consider a plane, so the ball trajectory will have two coordinates, which we name x^1,x^2 and we want to predict the next location of the ball, the prediction of future coordinates will be \u0177^1,\u0177^2. Let us represent this with a traditional neural network.<\/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-19d3a90 elementor-widget elementor-widget-image\" data-id=\"19d3a90\" 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=\"800\" height=\"365\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-04.png\" class=\"attachment-large size-large wp-image-80697\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-04.png 994w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-04-300x137.png 300w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-04-768x350.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-1e66819 elementor-widget elementor-widget-text-editor\" data-id=\"1e66819\" 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;\">Synthetically, if we posit (x^1,x^2)=x_t and (\u0177^1,\u0177^2)=\u0177_t, we can make the diagram below:<\/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-86336e4 elementor-widget elementor-widget-image\" data-id=\"86336e4\" 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=\"800\" height=\"389\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-05-1.png\" class=\"attachment-large size-large wp-image-80692\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-05-1.png 984w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-05-1-300x146.png 300w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-05-1-768x374.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-0a55c6e elementor-widget elementor-widget-text-editor\" data-id=\"0a55c6e\" 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;\">Here the index t indicates the coordinates of the ball at time t, representing the neural network by a function f, we have:<\/span><\/p><p><span style=\"font-weight: 400;\">f(x_t)=\u0177_t (resume the mathematical style of the RNN\u00a0 formula)<\/span><\/p><p><span style=\"font-weight: 400;\">As mentioned earlier, we are studying the <\/span><b>motion of the ball locally<\/b><span style=\"font-weight: 400;\">. We want to take into account several instants of the ball&#8217;s motion. If we can&#8217;t do this with a <\/span><b>traditional neural network<\/b><span style=\"font-weight: 400;\">, with RNNs, everything changes, because the concept of recurrence is introduced. Indeed, let us observe the figure below. We add an input <\/span><b>h_t<\/b><span style=\"font-weight: 400;\">, called hidden state. This hidden state embodies <\/span><b>\u0177_t <\/b><span style=\"font-weight: 400;\">and is given as an argument to the next prediction in addition to the input <\/span><b>x_t<\/b><span style=\"font-weight: 400;\">. We have indeed considered a set, and thus link outputs and inputs <\/span><b>without bound for our input samples<\/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-35bd674 elementor-widget elementor-widget-image\" data-id=\"35bd674\" 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=\"800\" height=\"620\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-06-1024x793.png\" class=\"attachment-large size-large wp-image-80696\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-06-1024x793.png 1024w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-06-300x232.png 300w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-06-768x595.png 768w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-06-1536x1190.png 1536w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-06.png 1695w\" 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-4894d3c elementor-widget elementor-widget-text-editor\" data-id=\"4894d3c\" 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;\">If we try to symbolize this with a formula, we get the following equation:<\/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-8572aca elementor-widget elementor-widget-image\" data-id=\"8572aca\" 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=\"800\" height=\"450\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/01\/Ajouter-un-titre-1024x576.jpg\" class=\"attachment-large size-large wp-image-166088\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/01\/Ajouter-un-titre-1024x576.jpg 1024w, https:\/\/datascientest.com\/en\/files\/2023\/01\/Ajouter-un-titre-300x169.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/01\/Ajouter-un-titre-768x432.jpg 768w, https:\/\/datascientest.com\/en\/files\/2023\/01\/Ajouter-un-titre-1536x864.jpg 1536w, https:\/\/datascientest.com\/en\/files\/2023\/01\/Ajouter-un-titre.jpg 1920w\" 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-550a563 elementor-widget elementor-widget-text-editor\" data-id=\"550a563\" 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;\">We can see the concept of <\/span><b>recurrence<\/b><span style=\"font-weight: 400;\">. To predict the next term, we need previous information from <\/span><i><b>h_{t-1}<\/b><\/i><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">The recurrence is even more obvious in this summary diagram.<\/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-a2f148b elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"a2f148b\" 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\">Learn to use predictive models<\/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-223b518 elementor-widget elementor-widget-spacer\" data-id=\"223b518\" 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-f0930dc e-flex e-con-boxed e-con\" data-id=\"f0930dc\" 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-2ad6cb1 elementor-widget elementor-widget-heading\" data-id=\"2ad6cb1\" 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 are the different applications of RNNs?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cfb46c1 elementor-widget elementor-widget-image\" data-id=\"cfb46c1\" 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=\"800\" height=\"335\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-07-1024x429.png\" class=\"attachment-large size-large wp-image-80695\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-07-1024x429.png 1024w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-07-300x126.png 300w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-07-768x322.png 768w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-07-1536x644.png 1536w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Sans-titre-1-Recupere-07-2048x859.png 2048w\" 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\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6fbd055 e-flex e-con-boxed e-con\" data-id=\"6fbd055\" 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-b57fb86 elementor-widget elementor-widget-text-editor\" data-id=\"b57fb86\" 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 aria-level=\"1\"><b>One to many<\/b><span style=\"font-weight: 400;\">, The RNN receives a single input and returns multiple outputs,\u00a0<\/span>the classic example of this process is the image legend<\/li><\/ul><ul><li aria-level=\"1\"><i><b>Many to one<\/b><\/i><span style=\"font-weight: 400;\"> There are <\/span><b>several inputs<\/b><span style=\"font-weight: 400;\"> and there is a <\/span><b>single output<\/b><span style=\"font-weight: 400;\">. An illustration of this mode is the sentiment analysis of texts. This makes it possible to identify a feeling from a group of words, determine the word that is missing to finish the sentence received as input.\u00a0<\/span><\/li><li aria-level=\"1\"><i><b>Many to many<\/b><\/i><span style=\"font-weight: 400;\">, Finally, you can take <\/span><b>several inputs<\/b><span style=\"font-weight: 400;\"> and get <\/span><b>several outputs<\/b><span style=\"font-weight: 400;\">. We don\u2019t necessarily have the same number of input and output neurons. We can cite, here, the translation of text, but we can be ambitious and plan to finish a musical work with its beginning.<\/span><\/li><\/ul><div>\u00a0<\/div><p><span style=\"font-weight: 400;\">Perfect, we have seen the <\/span><b>operation of an RNN <\/b><span style=\"font-weight: 400;\">and its <\/span><b>multiple applications<\/b><span style=\"font-weight: 400;\">, but is it perfect?\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Unfortunately, it has a major drawback, called <\/span><b>short-term memory<\/b><span style=\"font-weight: 400;\">, of which we will see an example in <\/span><b>ANLP <\/b><span style=\"font-weight: 400;\">(<\/span><i><span style=\"font-weight: 400;\">Automatic Natural Language Processing<\/span><\/i><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-ae3609c elementor-widget elementor-widget-spacer\" data-id=\"ae3609c\" 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-386072d e-flex e-con-boxed e-con\" data-id=\"386072d\" 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-46427d0 elementor-widget elementor-widget-heading\" data-id=\"46427d0\" 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\">Is the RNN a goldfish?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a8c8d25 elementor-widget elementor-widget-text-editor\" data-id=\"a8c8d25\" 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;\">Take the case of sentence completion.\u00a0<\/span><\/p><p style=\"padding-left: 25px;\"><em>&#8220;<strong>I like sushi, I\u2019m going to eat at the \u2026<\/strong>&#8220;<\/em><\/p><p><span style=\"font-weight: 400;\">The RNN can\u2019t remember the word sushi to predict <\/span><b>Japan<\/b><span style=\"font-weight: 400;\">, because they can\u2019t remember the word <\/span><i><b>sushi<\/b><\/i><span style=\"font-weight: 400;\">. In order to determine Japan, the RNN has to have a stronger memory. We can do that, by making neurons more complex.\u00a0 In particular, we will see the case of <\/span><b>LSTM<\/b><span style=\"font-weight: 400;\">(<\/span><i><span style=\"font-weight: 400;\">Long Short Term Memory<\/span><\/i><span style=\"font-weight: 400;\">). In addition to the conventional hidden state <\/span><i><span style=\"font-weight: 400;\">h_t<\/span><\/i><span style=\"font-weight: 400;\">, we will add a second state called<\/span><i><span style=\"font-weight: 400;\"> c_t.<\/span><\/i><span style=\"font-weight: 400;\"> Here,<\/span><i><span style=\"font-weight: 400;\"> h_t<\/span><\/i><span style=\"font-weight: 400;\"> represents the short memory of the neuron and<\/span><i><span style=\"font-weight: 400;\"> c_t <\/span><\/i><span style=\"font-weight: 400;\">the long memory.<\/span><\/p><p><span style=\"font-weight: 400;\">We will not go into the technical considerations of this frightening scheme. The main point is that we have a much more complex cell, which allows us to solve the memory problem. With the LSTM, we run the h_t and c_t through doors, 4 of them.<\/span><\/p><ul><li aria-level=\"1\"><span style=\"font-weight: 400;\">The first door <\/span><b>eliminates<\/b><span style=\"font-weight: 400;\"> unnecessary information, it\u2019s the forget gate.<\/span><\/li><li aria-level=\"1\"><span style=\"font-weight: 400;\">The second door, <\/span><b>stores <\/b><span style=\"font-weight: 400;\">the new information, the store gate.<\/span><\/li><li aria-level=\"1\"><span style=\"font-weight: 400;\">The third gate <\/span><b>updates<\/b><span style=\"font-weight: 400;\"> the information we will give to the RNN with the result of the forget gate and the store gate, it is the update gate.<\/span><\/li><li aria-level=\"1\"><span style=\"font-weight: 400;\">Finally, the last gate (output gate), <\/span><b>gives us <i>y_t<\/i> and<i> h_t<\/i><\/b><span style=\"font-weight: 400;\">.<\/span><\/li><\/ul><div>\u00a0<\/div><p><span style=\"font-weight: 400;\">This long process allows us to <\/span><b>control the information<\/b><span style=\"font-weight: 400;\"> we keep and transmit over time.<\/span><\/p><p><span style=\"font-weight: 400;\">The RNN manages to know what to keep and what to forget, thanks to its learning. The <\/span><b>LSTM<\/b><span style=\"font-weight: 400;\"> (Long Short Term Memory) is not unique, we can also use <\/span><b>GRU<\/b><span style=\"font-weight: 400;\"> (Gated Recurrent Unit), just the architecture of the cell changes.<\/span><\/p><p><span style=\"font-weight: 400;\">Let us now summarize what we have seen. <\/span><b>NRNs are a particular type of neural network<\/b><span style=\"font-weight: 400;\"> that can process data that are not independent and have no fixed size. However, standard NRNs are quite limited with the s<\/span><b>hort memory problem<\/b><span style=\"font-weight: 400;\">, which we can solve by using more complex cells like LSTM or GRU.<\/span><\/p><p><span style=\"font-weight: 400;\">In addition, we can draw a parallel with another neural network system: <\/span><b>convolution neural networks<\/b><span style=\"font-weight: 400;\"> (CNN). Indeed, NNCs are known to share spatial information while NRNs are known to share temporal information. Finally, if you want to put NRNs into practice, don\u2019t hesitate to <\/span><b>join one of our Data Scientist training<\/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-e798f13 elementor-widget elementor-widget-spacer\" data-id=\"e798f13\" 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<div class=\"elementor-element elementor-element-1dad0fc elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"1dad0fc\" 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\">Know more about our Data Scientist course<\/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-c582c8e e-flex e-con-boxed e-con\" data-id=\"c582c8e\" 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-47b8495 elementor-widget elementor-widget-html\" data-id=\"47b8495\" 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\": \"How to set up this neural network in situations ? \u200b\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Before explaining a RNN, let's focus on a bullet. Indeed, it is common in Machine Learning to want to predict the trajectory of a movable object.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"What are the different applications of RNNs?\u200b\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Many to one There are several inputs and there is a single output. An illustration of this mode is the sentiment analysis of texts. This makes it possible to identify a feeling from a group of words, determine the word that is missing to finish the sentence received as input.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Is the RNN a goldfish?\u200b\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"The RNN can\u2019t remember the word sushi to predict Japan, because they can\u2019t remember the word sushi. In order to determine Japan, the RNN has to have a stronger memory. We can do that, by making neurons more complex.  In particular, we will see the case of LSTM(Long Short Term Memory). In addition to the conventional hidden state h_t, we will add a second state called c_t. Here, h_t represents the short memory of the neuron and c_t the long memory.\"\r\n    }\r\n  }]\r\n}\r\n<\/script>\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\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>If you are a follower of our blog, you already know what a neural network is (if not, feel free to read this article first) but what does the adjective recurrent bring to this model? In this article, we will see how recurrent neural networks, called RNN, have become a classic model in deep learning. [&hellip;]<\/p>\n","protected":false},"author":63,"featured_media":80135,"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>Recurrent Neural Networks: The complete Guide<\/title>\n<meta name=\"description\" content=\"In this article, we will see how recurrent neural networks, called RNN, have become a classic model in deep learning.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/recurrent-neural-network-what-is-it\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta 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