{"id":198554,"date":"2025-10-14T06:43:00","date_gmt":"2025-10-14T05:43:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=198554"},"modified":"2025-10-14T19:44:14","modified_gmt":"2025-10-14T18:44:14","slug":"all-about-gated-recurrent-units","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/all-about-gated-recurrent-units","title":{"rendered":"Gated Recurrent Units (GRU): What are they? How to use them?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"198554\" class=\"elementor elementor-198554\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c3e5fb4 e-flex e-con-boxed e-con\" data-id=\"c3e5fb4\" 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-7084451 elementor-widget elementor-widget-text-editor\" data-id=\"7084451\" 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>For the effective processing of sequential data in Deep Learning and Machine Learning, understanding Gated Recurrent Units (GRUs) is crucial. These neural networks are capable of retaining critical information over time.<\/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-fb11789 elementor-widget elementor-widget-text-editor\" data-id=\"fb11789\" 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;\">Data with a <\/span><b>temporal nature<\/b><span style=\"font-weight: 400;\"> is everywhere: text, music, stock prices, sensor data&#8230; The meaning of each piece of data is influenced by preceding elements: a word gains its full meaning from the words that precede it in a sentence; predicting tomorrow&#8217;s temperature depends on today&#8217;s temperatures and those from previous days; or identifying a pattern in an audio signal requires analyzing the sound sequence across a period&#8230;<\/span><\/p><p><span style=\"font-weight: 400;\">Traditional neural networks are not suitable for handling sequences of this kind because they lack \u201c<\/span><b>memory<\/b><span style=\"font-weight: 400;\">\u201d.<\/span><\/p><p><span style=\"font-weight: 400;\">The initial solution for managing this type of data properly was <\/span><a href=\"https:\/\/datascientest.com\/en\/recurrent-neural-network-what-is-it\"><span style=\"font-weight: 400;\">Recurrent Neural Networks (RNNs)<\/span><\/a><span style=\"font-weight: 400;\">. However, they find it challenging to retain distant information due to the <\/span><b>vanishing gradient<\/b><span style=\"font-weight: 400;\"> (as gradients diminish with each layer of the RNN, they become so minuscule that they can no longer effectively update the weights of layers that are too far apart, thus losing information from the start of a sequence by the time it reaches its end).<\/span><\/p><p><span style=\"font-weight: 400;\">To address this critical limitation and enable Deep Learning networks to process long sequences efficiently, more advanced RNN architectures have been developed.<\/span><\/p><p><span style=\"font-weight: 400;\">Among the most effective and popular ones are <\/span><b>Gated Recurrent Units (GRUs)<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">GRUs are an improvement over simple RNNs, designed to better manage memory, and consequently, information transmission over extended periods.<\/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-5536fbb elementor-widget elementor-widget-image\" data-id=\"5536fbb\" 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=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-1.webp\" class=\"attachment-full size-full wp-image-198557\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-1.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-1-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-1-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-1-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\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-349a9d3 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"349a9d3\" 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 elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-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\">Understand machine learning<\/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-395b4f3 e-flex e-con-boxed e-con\" data-id=\"395b4f3\" 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-eca8328 elementor-widget elementor-widget-heading\" data-id=\"eca8328\" 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\">The Key Mechanism: Gating Mechanism<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a03ce4e elementor-widget elementor-widget-text-editor\" data-id=\"a03ce4e\" 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 primary innovation of GRUs, and RNNs from their family (such as LSTMs), is in the use of <\/span><b>\u201cgates\u201d (gating mechanism)<\/b><span style=\"font-weight: 400;\">. Rather than just blending the current input and the previous hidden state (h t-1) indiscriminately, as a simple RNN does, GRUs utilize these gates to <\/span><b>selectively and dynamically regulate the flow of information<\/b><span style=\"font-weight: 400;\"> through the recurrent unit.<\/span><\/p><p><span style=\"font-weight: 400;\">These gates act as filters, based on activation functions (such as the sigmoid function), to determine which information from the past memory and the new input is most significant.<\/span><\/p><p><span style=\"font-weight: 400;\">A GRU uses two essential gates:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Reset Gate:<\/b><span style=\"font-weight: 400;\"> This gate decides which part of the previous hidden state is <\/span><b>ignored<\/b><span style=\"font-weight: 400;\"> or \u201cforgotten\u201d when calculating a potential candidate state for the new hidden state.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Update Gate:<\/b><span style=\"font-weight: 400;\"> Determines how much of the previous hidden state should be <\/span><b>retained<\/b><span style=\"font-weight: 400;\"> and <\/span><b>how much of the new candidate state<\/b><span style=\"font-weight: 400;\"> (which represents the new potential information to integrate) should be <\/span><b>added<\/b><span style=\"font-weight: 400;\"> to form the new hidden state.<\/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-f625ed7 elementor-widget elementor-widget-text-editor\" data-id=\"f625ed7\" 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;\">These mechanisms enable the GRU to preserve or discard information over long durations.<\/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-06de871 e-flex e-con-boxed e-con\" data-id=\"06de871\" 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-54127d0 elementor-widget elementor-widget-heading\" data-id=\"54127d0\" 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\">GRU vs LSTM<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-72ecc04 elementor-widget elementor-widget-text-editor\" data-id=\"72ecc04\" 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>Long Short Term Memory (LSTMs) <\/b><span style=\"font-weight: 400;\">are very similar to GRUs, as they also employ \u201cgates\u201d to manage long-term memory and alleviate the vanishing gradient problem.<\/span><\/p><p><span style=\"font-weight: 400;\">The difference with GRUs is that LSTMs use three gates, whereas GRUs use only two, thus making them more complex and slower to train.<\/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-85f6894 elementor-widget elementor-widget-image\" data-id=\"85f6894\" 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=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-2.webp\" class=\"attachment-full size-full wp-image-198558\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-2.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-2-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-2-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-2-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\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-5600fd4 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"5600fd4\" 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 elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-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\">Master GRUs<\/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-7684858 e-flex e-con-boxed e-con\" data-id=\"7684858\" 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-fba8a2d elementor-widget elementor-widget-heading\" data-id=\"fba8a2d\" 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\">The Advantages of GRUs<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d61d46b elementor-widget elementor-widget-text-editor\" data-id=\"d61d46b\" 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 primary advantage of GRUs over traditional RNNs lies in their ability to effectively capture dependencies between data in the same series, including more distant ones. In fact, thanks to the gates, the GRU can selectively retain relevant information over very long sequences and disregard information that is not.<\/span><\/p><p><span style=\"font-weight: 400;\">For instance, in a lengthy sentence, a GRU can learn to keep the main subject and verb in memory, even if they are separated by numerous words, to ensure grammatical and semantic coherence by the end of the sentence. This ability makes them particularly effective at understanding context in long data sequences.<\/span><\/p><p><span style=\"font-weight: 400;\">These gates allow gradients to pass more directly through time, preventing them from becoming too small (vanishing) as they are propagated back to the network&#8217;s initial layers. This stabilizes <\/span><b>training<\/b><span style=\"font-weight: 400;\"> and enables the network to effectively learn the <\/span><b>weights<\/b><span style=\"font-weight: 400;\"> that capture long-term dependencies.<\/span><\/p><p><span style=\"font-weight: 400;\">LSTMs adhere to these principles as well. However, GRUs have a simpler architecture with fewer parameters (fewer gates) compared to LSTMs. This results in enhanced computational efficiency: they are generally faster to train and execute while delivering top performance on a wide array of sequential tasks, sometimes even surpassing LSTMs.<\/span><\/p><p><span style=\"font-weight: 400;\">This is a significant advantage for real-time applications or when computing resources are constrained.<\/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-fca821a elementor-widget elementor-widget-image\" data-id=\"fca821a\" 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=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-3.webp\" class=\"attachment-full size-full wp-image-198559\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-3.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-3-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-3-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-3-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\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-02875e5 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"02875e5\" 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 elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-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\">Become a expert in GRUs<\/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-a5c983c e-flex e-con-boxed e-con\" data-id=\"a5c983c\" 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-015235c elementor-widget elementor-widget-heading\" data-id=\"015235c\" 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 Applications?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-98f7087 elementor-widget elementor-widget-text-editor\" data-id=\"98f7087\" 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 benefits of GRUs pave the way for various concrete applications across different fields:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\"><b>Natural Language Processing (NLP)<\/b><\/a><b>:<\/b><span style=\"font-weight: 400;\"> GRUs are widely used for tasks such as machine translation, text generation, sentiment analysis, speech recognition, and other tasks where understanding the sequential context is crucial.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b><a href=\"https:\/\/datascientest.com\/en\/time-series-daniel-can-you-tell-us-about-them\">Time Series<\/a>:<\/b><span style=\"font-weight: 400;\"> They are also effective for predicting future values based on historical data (financial forecasts, weather predictions, energy consumption forecasts, etc.).<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Diverse Sequence Modeling:<\/b><span style=\"font-weight: 400;\"> Lastly, they are regularly used for generating music, genomes, and any other data that presents itself in an ordered sequence.<\/span><\/li><\/ul>\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-dc46e52 e-flex e-con-boxed e-con\" data-id=\"dc46e52\" 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-8ef89d0 elementor-widget elementor-widget-heading\" data-id=\"8ef89d0\" 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-ab16a1f elementor-widget elementor-widget-text-editor\" data-id=\"ab16a1f\" 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 summary, <\/span><b>Gated Recurrent Units (GRUs)<\/b><span style=\"font-weight: 400;\"> represent a significant advancement over simple <\/span><b>Recurrent Neural Networks (RNNs)<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Thanks to their intelligent gating mechanisms, they effectively manage long-term memory and overcome the training challenges of simple RNNs, such as the vanishing gradient.<\/span><\/p><p><span style=\"font-weight: 400;\">Their efficiency, robustness, and impressive performance make them a popular choice in <a href=\"https:\/\/datascientest.com\/en\/unraveling-machine-learning-vs-deep-learning-key-differences-explained\">Deep Learning and Machine Learning<\/a> for a variety of applications.<\/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-54a67ae elementor-widget elementor-widget-image\" data-id=\"54a67ae\" 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=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-4.webp\" class=\"attachment-full size-full wp-image-198560\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-4.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/08\/Gated-Recurrent-Units-DataScientest-4-300x200.webp 300w, 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These neural networks are capable of retaining critical information over time. Data with a temporal nature is everywhere: text, music, stock prices, sensor data&#8230; The meaning of each piece of data is influenced by preceding [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":198556,"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>Gated Recurrent Units (GRU): What are they? How to use them?<\/title>\n<meta name=\"description\" content=\"Discover the Gated Recurrent Units (GRUs), a Deep Learning architecture designed to process sequential data. 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