{"id":170478,"date":"2023-09-10T15:53:02","date_gmt":"2023-09-10T14:53:02","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=170478"},"modified":"2024-11-27T15:10:55","modified_gmt":"2024-11-27T14:10:55","slug":"convolutional-neural-network-everything-you-need-to-know","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/convolutional-neural-network-everything-you-need-to-know","title":{"rendered":"Convolutional Neural Network: Everything You Need to Know"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"170478\" class=\"elementor elementor-170478\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-01af06c e-flex e-con-boxed e-con\" data-id=\"01af06c\" 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-6483ad2d elementor-widget elementor-widget-heading\" data-id=\"6483ad2d\" 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-default\">In this section, we will focus on one of the most powerful algorithms in Deep Learning: Convolutional Neural Networks (CNNs). These are powerful programming models that enable image recognition, in particular, by automatically assigning a label to each image provided as input, corresponding to the class to which it belongs.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7cf266e elementor-widget elementor-widget-text-editor\" data-id=\"7cf266e\" 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>Welcome to the third episode of our Deep Learning series. After introducing Deep Learning and its applications in the first part, and diving into the structure and operation of <a href=\"https:\/\/datascientest.com\/en\/recurrent-neural-network-what-is-it\">neural networks<\/a> in the second, let&#8217;s continue our journey!<\/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-22c29cc2 elementor-widget__width-initial elementor-widget elementor-widget-video\" data-id=\"22c29cc2\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/ugALxRuTh00&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\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-224cb85 e-flex e-con-boxed e-con\" data-id=\"224cb85\" 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-caf1882 elementor-widget elementor-widget-text-editor\" data-id=\"caf1882\" 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>Table of Contents:<\/p><ol><li><a href=\"#architecturecnn\">Architecture of a Convolutional Neural Network-CNN<\/a><\/li><li><a href=\"#applications\">What are the main applications of CNNs?<\/a><\/li><li><a href=\"#partieconvolutive\">Convolutional Part<\/a><\/li><li><a href=\"#maxpoooling\">Max Pooling<\/a><\/li><li><a href=\"#miseenapplicationcnn\">Application of CNN<\/a><\/li><\/ol>\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-4f7b4a5 e-flex e-con-boxed e-con\" data-id=\"4f7b4a5\" 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<div class=\"elementor-element elementor-element-5a85dcf6 e-con-full e-flex e-con\" data-id=\"5a85dcf6\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-278aeca8 elementor-widget elementor-widget-image\" data-id=\"278aeca8\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/datascientest.com\/catalog\/module\/151-introduction-au-deeplearning-avec-le-framework-keras\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"370\" height=\"264\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-1.png\" class=\"attachment-full size-full wp-image-170482\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-1.png 370w, https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-1-300x214.png 300w\" sizes=\"(max-width: 370px) 100vw, 370px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Recognition of a Santa Claus by a CNN model<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\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-e6d4bdd e-flex e-con-boxed e-con\" data-id=\"e6d4bdd\" 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-09b6beb elementor-widget elementor-widget-menu-anchor\" data-id=\"09b6beb\" data-element_type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\nbody.elementor-page .elementor-widget-menu-anchor{margin-bottom:0}<\/style>\t\t<div id=\"applications\" class=\"elementor-menu-anchor\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-59f7191 elementor-widget elementor-widget-heading\" data-id=\"59f7191\" 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 main applications for CNNs?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aae90c9 elementor-widget elementor-widget-text-editor\" data-id=\"aae90c9\" 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>A<strong> convolutional neural network<\/strong> draws inspiration from nature, as the connectivity between artificial neurons resembles the organization of the animal visual cortex.<\/p><p>One of its primary use cases is image recognition.<strong> Convolutional networks<\/strong> learn faster and achieve lower error rates in this domain. They are also used, to a lesser extent, for video analysis.<\/p><p>This type of network is employed in natural language processing as well. CNN models are highly effective for semantic analysis, sentence modeling, classification, and translation.<\/p><p>Compared to traditional methods like recurrent neural networks, convolutional neural networks can represent different contextual realities of language without relying on a sequential assumption.<\/p><p>Convolutional networks have also been applied in drug discovery. They aid in identifying potential treatments by predicting interactions between molecules and biological proteins.<\/p><p>CNNs have notably been used in game software for Go and chess, where they can excel. Another application is anomaly detection in input images.<\/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-93813fd e-flex e-con-boxed e-con\" data-id=\"93813fd\" 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<div class=\"elementor-element elementor-element-b67d349 e-con-full e-flex e-con\" data-id=\"b67d349\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2bffadf elementor-widget elementor-widget-menu-anchor\" data-id=\"2bffadf\" data-element_type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div id=\"architecturecnn\" class=\"elementor-menu-anchor\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c49a256 elementor-widget elementor-widget-heading\" data-id=\"c49a256\" 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\">Convolutional Neural Network-CNN architecture<\/h3>\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-6ebfde0 e-flex e-con-boxed e-con\" data-id=\"6ebfde0\" 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<div class=\"elementor-element elementor-element-45b55b5 e-con-full e-flex e-con\" data-id=\"45b55b5\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-208f806 elementor-widget elementor-widget-text-editor\" data-id=\"208f806\" 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<blockquote><p><strong>Convolutional Neural Networks (CNNs)<\/strong><a href=\"https:\/\/datascientest.com\/en\/deep-neural-network-what-is-it-and-how-is-it-working\"> constitute a subcategory of neural networks<\/a> and are currently one of the most renowned models for image classification due to their exceptional performance.<\/p><\/blockquote>\t\t\t\t\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-b015620 e-flex e-con-boxed e-con\" data-id=\"b015620\" 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<div class=\"elementor-element elementor-element-0769e19 e-con-full e-flex e-con\" data-id=\"0769e19\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7bad5cf elementor-widget elementor-widget-text-editor\" data-id=\"7bad5cf\" 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>Their mode of operation may seem simple at first glance: the user provides an input image in the form of a pixel matrix. This image has three dimensions:<\/p><p>Two dimensions for a grayscale image.<br \/>A third dimension with a depth of 3 to represent the fundamental colors (Red, Green, Blue).<\/p>\t\t\t\t\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-b0e337b e-flex e-con-boxed e-con\" data-id=\"b0e337b\" 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<div class=\"elementor-element elementor-element-04f3ef2 e-con-full e-flex e-con\" data-id=\"04f3ef2\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c76db12 elementor-widget elementor-widget-text-editor\" data-id=\"c76db12\" 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>Unlike a standard Multi-Layer Perceptron (MLP) model, which contains only a classification part, the architecture of the Convolutional Neural Network has two distinct parts:<\/p><p><strong>1. Convolutional Part: I<\/strong>ts ultimate goal is to extract specific features from each image by compressing them to reduce their initial size. In summary, the input image goes through a series of filters, creating new images called convolution maps. Finally, the obtained convolution maps are concatenated into a feature vector called the CNN code.<\/p><p><strong>2. Classification Part:<\/strong> The CNN code obtained as the output of the convolutional part is fed into a second part, consisting of fully connected layers called a Multi-Layer Perceptron (MLP). The role of this part is to combine the features of the CNN code to classify the image. For more information on this part, feel free to refer to the article on the subject.<\/p>\t\t\t\t\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-1667dab e-flex e-con-boxed e-con\" data-id=\"1667dab\" 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<div class=\"elementor-element elementor-element-3537065 e-con-full e-flex e-con\" data-id=\"3537065\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5a06f7a elementor-widget elementor-widget-menu-anchor\" data-id=\"5a06f7a\" data-element_type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div id=\"partieconvolutive\" class=\"elementor-menu-anchor\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-15dc438 elementor-widget elementor-widget-heading\" data-id=\"15dc438\" 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\">Convolution part: What is convolution used for?<\/h3>\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-400ac9b e-flex e-con-boxed e-con\" data-id=\"400ac9b\" 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<div class=\"elementor-element elementor-element-50d9710 e-con-full e-flex e-con\" data-id=\"50d9710\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0d96171 elementor-widget elementor-widget-heading\" data-id=\"0d96171\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">First of all, what is convolution used for?<\/h4>\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-7a1bd3a e-flex e-con-boxed e-con\" data-id=\"7a1bd3a\" 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<div class=\"elementor-element elementor-element-2d578b8 e-con-full e-flex e-con\" data-id=\"2d578b8\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-22564bbf elementor-widget elementor-widget-text-editor\" data-id=\"22564bbf\" 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>Convolution is a simple mathematical operation generally used for image processing and recognition. Its effect on an image is similar to filtering, which works as follows:<\/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-7544a682 elementor-widget elementor-widget-image\" data-id=\"7544a682\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.natural-solutions.eu\/blog\/la-reconnaissance-dimage-avec-les-rseaux-de-neurones-convolutifs#:~:text=Qu&#039;est%2Dce%20que%20la,%C3%A0%20la%20reconnaissance%20d&#039;image.\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1201\" height=\"744\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Fichier-98.png\" class=\"attachment-full size-full wp-image-170484\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Fichier-98.png 1201w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Fichier-98-300x186.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Fichier-98-1024x634.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Fichier-98-768x476.png 768w\" sizes=\"(max-width: 1201px) 100vw, 1201px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Diagram of how the filter window moves across the image<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\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-1c30640 e-flex e-con-boxed e-con\" data-id=\"1c30640\" 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<div class=\"elementor-element elementor-element-c788fb8 e-con-full e-flex e-con\" data-id=\"c788fb8\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a2c7c12 elementor-widget elementor-widget-text-editor\" data-id=\"a2c7c12\" 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<ol><li><p>Initially, you define the size of the filter window, typically starting from the top-left corner.<\/p><p>The filter window, representing a feature, moves progressively from left to right by a predetermined number of cells (the stride) until it reaches the end of the image.<\/p><p>For each encountered portion of the image, a convolution calculation is performed, resulting in an output activation map or feature map that indicates where the features are located in the image. The higher the value in the feature map, the more the scanned portion of the image resembles the feature being sought. This process helps CNNs identify meaningful patterns and features within the input image.<\/p><\/li><\/ol>\t\t\t\t\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-e53a0ce e-flex e-con-boxed e-con\" data-id=\"e53a0ce\" 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<div class=\"elementor-element elementor-element-2e42a1d e-con-full e-flex e-con\" data-id=\"2e42a1d\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2a3ffb8 elementor-widget elementor-widget-heading\" data-id=\"2a3ffb8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Example of a classical convolution filter\n<\/h4>\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-7e50b6d e-flex e-con-boxed e-con\" data-id=\"7e50b6d\" 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<div class=\"elementor-element elementor-element-10d34f6 e-con-full e-flex e-con\" data-id=\"10d34f6\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3a9e12a elementor-widget elementor-widget-text-editor\" data-id=\"3a9e12a\" 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>During the<strong> convolutional part of a Convolutional Neural Network<\/strong>, the input image passes through a sequence of convolution filters. For example, there are commonly used convolution filters that are designed to extract more meaningful features than individual pixels, such as edge detection (derivative filter) or geometric shape detection. The choice and application of these filters are automatically determined by the model.<\/p><p>Among the most well-known filters are the averaging filter (which computes the average of each pixel with its 8 neighboring pixels) and the Gaussian filter (which is used to reduce noise in an input image). Here&#8217;s an example of the effects of these two different filters on an image with significant noise (imagine a photograph taken in low light conditions, for example). However, one drawback of noise reduction is that it often comes with a reduction in sharpness.<\/p>\t\t\t\t\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-7e98e1f e-flex e-con-boxed e-con\" data-id=\"7e98e1f\" 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<div class=\"elementor-element elementor-element-5eebdcdf e-con-full e-flex e-con\" data-id=\"5eebdcdf\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-342259bb elementor-widget elementor-widget-image\" data-id=\"342259bb\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"512\" height=\"306\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-4.png\" class=\"attachment-full size-full wp-image-170485\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-4.png 512w, https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-4-300x179.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Effect of averaging and Gaussian filters - DataScientest<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\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-cae377c e-flex e-con-boxed e-con\" data-id=\"cae377c\" 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<div class=\"elementor-element elementor-element-42ddb37 e-con-full e-flex e-con\" data-id=\"42ddb37\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-be717a8 elementor-widget elementor-widget-text-editor\" data-id=\"be717a8\" 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>As you can see, unlike the averaging filter, the Gaussian filter reduces noise without significantly reducing sharpness.<\/p><p>In addition to its filtering function, the convolutional part of a CNN is valuable because it extracts features unique to each image while compressing them to reduce their initial size, using subsampling methods like Max-Pooling.<\/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-43e8661 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"43e8661\" 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\/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\">Become a Data Scientist<\/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>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3c1127d e-flex e-con-boxed e-con\" data-id=\"3c1127d\" 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<div class=\"elementor-element elementor-element-27c0946 e-con-full e-flex e-con\" data-id=\"27c0946\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8f8a448 elementor-widget elementor-widget-menu-anchor\" data-id=\"8f8a448\" data-element_type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div id=\"maxpoooling\" class=\"elementor-menu-anchor\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8f9ea46 elementor-widget elementor-widget-heading\" data-id=\"8f9ea46\" 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\">Sub-sampling method: Max-Pooling<\/h3>\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-699c6d9 e-flex e-con-boxed e-con\" data-id=\"699c6d9\" 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<div class=\"elementor-element elementor-element-f759b45 e-con-full e-flex e-con\" data-id=\"f759b45\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-acbff6f elementor-widget elementor-widget-text-editor\" data-id=\"acbff6f\" 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><strong>Max-Pooling<\/strong> is a subsampling process based on sampling. Its goal is to downsample an input representation (image, hidden layer output matrix, etc.) by reducing its dimension. Additionally, it reduces computational cost by reducing the number of parameters to learn and provides invariance to small translations (if a small translation does not change the maximum value in the sampled region, the maximum value in each region will remain the same, and therefore, the newly created matrix will remain identical).<\/p><p>To make the action of Max-Pooling more concrete, let&#8217;s consider an example: imagine we have a 4&#215;4 matrix representing our initial input and a 2&#215;2 window filter that we apply to our input. For each of the regions scanned by the filter, Max-Pooling takes the maximum value, thus creating a new output matrix where each element corresponds to the maximum value in each encountered region.<\/p>\t\t\t\t\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-989f84d e-flex e-con-boxed e-con\" data-id=\"989f84d\" 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<div class=\"elementor-element elementor-element-236716c8 e-con-full e-flex e-con\" data-id=\"236716c8\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-194d2fff elementor-widget elementor-widget-text-editor\" data-id=\"194d2fff\" 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;\">Let&#8217;s illustrate the process:<\/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-2a231d00 elementor-widget elementor-widget-image\" data-id=\"2a231d00\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/cs231n.github.io\/convolutional-networks\/\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1201\" height=\"543\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Fichier-97.png\" class=\"attachment-full size-full wp-image-170486\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Fichier-97.png 1201w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Fichier-97-300x136.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Fichier-97-1024x463.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Fichier-97-768x347.png 768w\" sizes=\"(max-width: 1201px) 100vw, 1201px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Max-Pooling process<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\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-7dbd5ce e-con-full e-flex e-con\" data-id=\"7dbd5ce\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-82d826d elementor-widget elementor-widget-text-editor\" data-id=\"82d826d\" 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 filter window moves two pixels to the right (stride\/step = 2) and at each step retrieves the &#8220;argmax&#8221; corresponding to the largest of the 4 pixel values.<\/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-8ff6b1f elementor-widget elementor-widget-image\" data-id=\"8ff6b1f\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/cs231n.github.io\/convolutional-networks\/\">\n\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/06\/Fichier-96.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Example of the effect of Max-Pooling<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6635ff9 e-flex e-con-boxed e-con\" data-id=\"6635ff9\" 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<div class=\"elementor-element elementor-element-0f0c176 e-con-full e-flex e-con\" data-id=\"0f0c176\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d037818 elementor-widget elementor-widget-text-editor\" data-id=\"d037818\" 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 purpose of the convolutional part of a CNN becomes clearer: unlike a traditional MLP model, adding the convolutional part upstream allows us to obtain an output &#8220;feature map&#8221; or &#8220;CNN code&#8221; (the pixel matrix on the right in the example) with smaller dimensions than those of the original image. This significantly reduces the number of parameters to be computed in the model.<\/p>\t\t\t\t\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-d57e947 e-flex e-con-boxed e-con\" data-id=\"d57e947\" 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<div class=\"elementor-element elementor-element-694cce4 e-con-full e-flex e-con\" data-id=\"694cce4\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-20864ec elementor-widget elementor-widget-heading\" data-id=\"20864ec\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Example of a CNN architecture and its output<\/h4>\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-5ec1cfe e-flex e-con-boxed e-con\" data-id=\"5ec1cfe\" 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<div class=\"elementor-element elementor-element-447746c7 e-con-full e-flex e-con\" data-id=\"447746c7\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5bdad0f elementor-widget elementor-widget-text-editor\" data-id=\"5bdad0f\" 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>After the convolutional part of a CNN comes the classification part. This classification part, common to all neural network models, corresponds to a Multi-Layer Perceptron (MLP) model.<\/p><p>Its goal is to assign a label describing its class membership to each data sample.<\/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-5c5d18be elementor-widget elementor-widget-image\" data-id=\"5c5d18be\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.natural-solutions.eu\/blog\/histoire-du-deep-learning#:~:text=Les%20perceptrons%20multicouches%3A%20MLP%20(1986)&#038;text=Les%20perceptrons%20multicouches%20ont%20pour,mieux%20cette%20base%20de%20donn%C3%A9es.\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1033\" height=\"767\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Fichier-95.png\" class=\"attachment-full size-full wp-image-170488\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Fichier-95.png 1033w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Fichier-95-300x223.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Fichier-95-1024x760.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Fichier-95-768x570.png 768w\" sizes=\"(max-width: 1033px) 100vw, 1033px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Representation of a multilayer perceptron<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\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-48946b3 e-flex e-con-boxed e-con\" data-id=\"48946b3\" 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<div class=\"elementor-element elementor-element-35683f7a e-con-full e-flex e-con\" data-id=\"35683f7a\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6ae6bf12 elementor-widget elementor-widget-text-editor\" data-id=\"6ae6bf12\" 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 algorithm that perceptrons use to update their weights (or network coefficients) is called backpropagation of the error gradient, a famous gradient descent algorithm that we will delve into in more detail later on.<\/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-23fee720 elementor-widget elementor-widget-image\" data-id=\"23fee720\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/dspace.univ-tlemcen.dz\/bitstream\/112\/12235\/1\/Classification-des-images-avec-les-reseaux-de-neurones.pdf\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"2033\" height=\"540\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/convolution-pooling.png\" class=\"attachment-full size-full wp-image-170489\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/convolution-pooling.png 2033w, https:\/\/datascientest.com\/en\/files\/2023\/09\/convolution-pooling-300x80.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/09\/convolution-pooling-1024x272.png 1024w, https:\/\/datascientest.com\/en\/files\/2023\/09\/convolution-pooling-768x204.png 768w, https:\/\/datascientest.com\/en\/files\/2023\/09\/convolution-pooling-1536x408.png 1536w\" sizes=\"(max-width: 2033px) 100vw, 2033px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Example of a CNN architecture<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\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-ae54d06 e-flex e-con-boxed e-con\" data-id=\"ae54d06\" 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<div class=\"elementor-element elementor-element-302af63 e-con-full e-flex e-con\" data-id=\"302af63\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-fbd574f elementor-widget elementor-widget-text-editor\" data-id=\"fbd574f\" 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>Generally, the architecture of a Convolutional Neural Network is quite similar:<\/p><p><strong>1. Convolutional Layer (CONV):<\/strong> The role of this first layer is to analyze the input images and detect the presence of a set of features. It produces a set of feature maps as output (as explained earlier: what is the purpose of convolution?).<br \/><strong>2. Pooling Layer (POOL):<\/strong> The pooling layer is typically applied between two convolutional layers. It takes the feature maps generated by the convolutional layer as input and aims to reduce the size of the images while preserving their essential characteristics. Among the most commonly used pooling methods are max-pooling (mentioned earlier) and average pooling, which calculates the average value of the filter window at each step.<br \/><strong>3. Rectified Linear Units (ReLU) Activation Layer:<\/strong> This layer replaces all negative input values with zeros. The purpose of these activation layers is to make the model nonlinear and, therefore, more complex.<\/p><p>The final output of the pooling layer retains the same number of feature maps as input but in a considerably compressed form.<\/p>\t\t\t\t\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-ebed5b5 e-flex e-con-boxed e-con\" data-id=\"ebed5b5\" 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<div class=\"elementor-element elementor-element-6cd09c71 e-con-full e-flex e-con\" data-id=\"6cd09c71\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1f1c2a27 elementor-widget elementor-widget-image\" data-id=\"1f1c2a27\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/ml4a.github.io\/ml4a\/fr\/neural_networks\/\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"274\" height=\"228\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-9.png\" class=\"attachment-full size-full wp-image-170490\" alt=\"\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">ReLU activation function<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\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-cb10400 e-flex e-con-boxed e-con\" data-id=\"cb10400\" 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<div class=\"elementor-element elementor-element-e6ddacd e-con-full e-flex e-con\" data-id=\"e6ddacd\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-57a876e elementor-widget elementor-widget-text-editor\" data-id=\"57a876e\" 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><strong>Fully Connected (FC) Layer:<\/strong> These layers are placed at the end of the CNN architecture and are fully connected to all output neurons (hence the term fully-connected). After receiving an input vector, the FC layer applies a linear combination followed by an activation function, ultimately aiming to classify the input image (see the following diagram). In the end, it outputs a vector of size d, corresponding to the number of classes, where each component represents the probability of the input image belonging to a class.<\/li><\/ul>\t\t\t\t\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-cc6fc95 e-flex e-con-boxed e-con\" data-id=\"cc6fc95\" 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<div class=\"elementor-element elementor-element-2f77d2f5 e-con-full e-flex e-con\" data-id=\"2f77d2f5\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-50e747d3 elementor-widget elementor-widget-image\" data-id=\"50e747d3\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"512\" height=\"295\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-10.png\" class=\"attachment-full size-full wp-image-170491\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-10.png 512w, https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-10-300x173.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Operation of a neural network with 2 hidden layers<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\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-cfe0f48 e-flex e-con-boxed e-con\" data-id=\"cfe0f48\" 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<div class=\"elementor-element elementor-element-14a2abf e-con-full e-flex e-con\" data-id=\"14a2abf\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e15ce9b elementor-widget elementor-widget-heading\" data-id=\"e15ce9b\" 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\">Implementation of a pre-trained CNN on Python : <\/h3>\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-4ae8e9b e-flex e-con-boxed e-con\" data-id=\"4ae8e9b\" 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<div class=\"elementor-element elementor-element-de5b7bb e-con-full e-flex e-con\" data-id=\"de5b7bb\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f8890da elementor-widget elementor-widget-text-editor\" data-id=\"f8890da\" 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>For practical purposes and given the complexity of creating effective CNNs from scratch, we will use pre-trained networks available in the Torchvision module. Let&#8217;s see how this can be implemented in <a href=\"https:\/\/datascientest.com\/en\/python-programming-for-beginners-episode-3\">Python:<\/a><\/p>\t\t\t\t\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-33c39eb e-flex e-con-boxed e-con\" data-id=\"33c39eb\" 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<div class=\"elementor-element elementor-element-636e125 e-con-full e-flex e-con\" data-id=\"636e125\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ce20a7b elementor-widget elementor-widget-menu-anchor\" data-id=\"ce20a7b\" data-element_type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div id=\"miseenapplicationcnn\" class=\"elementor-menu-anchor\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1e91c22 elementor-widget elementor-widget-heading\" data-id=\"1e91c22\" 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\">Application of a CNN: Identification of any images from the ImageNet dataset: ten million labelled images<\/h3>\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-3cd17d8 e-flex e-con-boxed e-con\" data-id=\"3cd17d8\" 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<div class=\"elementor-element elementor-element-e13dd8f e-con-full e-flex e-con\" data-id=\"e13dd8f\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-959ebe0 elementor-widget elementor-widget-text-editor\" data-id=\"959ebe0\" 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>ImageNet is a database of over ten million labeled images produced by the organization of the same name, intended for computer vision research. Here is an excerpt from this extensive dataset:<\/p>\t\t\t\t\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-650416a e-flex e-con-boxed e-con\" data-id=\"650416a\" 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<div class=\"elementor-element elementor-element-5f95bf4a e-con-full e-flex e-con\" data-id=\"5f95bf4a\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7ffd6520 elementor-widget elementor-widget-image\" data-id=\"7ffd6520\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/machinelearningmastery.com\/use-pre-trained-vgg-model-classify-objects-photographs\/\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"442\" height=\"272\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-11.png\" class=\"attachment-full size-full wp-image-170492\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-11.png 442w, https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-11-300x185.png 300w\" sizes=\"(max-width: 442px) 100vw, 442px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Extract from the ImageNet database<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-31a7c638 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"31a7c638\" 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\/formation-data-scientist\">\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\">D\u00e9couvrir notre formation de Data Scientist<\/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>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ebb0d8b e-con-full e-flex e-con\" data-id=\"ebb0d8b\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0fc9fb1 elementor-widget elementor-widget-text-editor\" data-id=\"0fc9fb1\" 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>Step 0: First of all, let&#8217;s import all the libraries we&#8217;ll need for the rest of the process<\/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-761f163 elementor-widget elementor-widget-shortcode\" data-id=\"761f163\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/fa94546ddc37a047de6c3135b9d726ef.js\"><\/script>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c0a2923 e-flex e-con-boxed e-con\" data-id=\"c0a2923\" 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<div class=\"elementor-element elementor-element-a43cabf e-con-full e-flex e-con\" data-id=\"a43cabf\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a449473 elementor-widget elementor-widget-text-editor\" data-id=\"a449473\" 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>Step 1: Training the VGG16 pre-trained model<\/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-022280e elementor-widget elementor-widget-shortcode\" data-id=\"022280e\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/4b4bf05a7373eb02de7279fd09c1c065.js\"><\/script><\/div>\n\t\t\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-d0287c4 e-flex e-con-boxed e-con\" data-id=\"d0287c4\" 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<div class=\"elementor-element elementor-element-bdb3604 e-con-full e-flex e-con\" data-id=\"bdb3604\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-095368e elementor-widget elementor-widget-text-editor\" data-id=\"095368e\" 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>Step 2: Importing the 3 images to be predicted<\/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-71eb23c elementor-widget elementor-widget-shortcode\" data-id=\"71eb23c\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/cef25c1c0944feefdff693be31c9d70e.js\"><\/script><\/div>\n\t\t\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-116a476 e-flex e-con-boxed e-con\" data-id=\"116a476\" 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<div class=\"elementor-element elementor-element-3cdbbecf e-con-full e-flex e-con\" data-id=\"3cdbbecf\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-57f6d645 elementor-widget elementor-widget-image\" data-id=\"57f6d645\" 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\t<a href=\"https:\/\/www.amazon.fr\/Ballon-football-Taille-standard-blanc\/dp\/B018URJZPK\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"275\" height=\"183\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-12.png\" class=\"attachment-large size-large wp-image-170493\" alt=\"\" \/>\t\t\t\t\t\t\t\t<\/a>\n\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>\n\t\t<div class=\"elementor-element elementor-element-27a536c9 e-con-full e-flex e-con\" data-id=\"27a536c9\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5f584c5a elementor-widget elementor-widget-image\" data-id=\"5f584c5a\" 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\t<a href=\"https:\/\/fr.wikipedia.org\/wiki\/Canis_lupus\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"533\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Canis_lupus_Ernstbrunn-1024x682.jpg\" class=\"attachment-large size-large wp-image-170494\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/Canis_lupus_Ernstbrunn-1024x682.jpg 1024w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Canis_lupus_Ernstbrunn-300x200.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Canis_lupus_Ernstbrunn-768x512.jpg 768w, https:\/\/datascientest.com\/en\/files\/2023\/09\/Canis_lupus_Ernstbrunn.jpg 1280w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\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>\n\t\t<div class=\"elementor-element elementor-element-2f40f2bd e-con-full e-flex e-con\" data-id=\"2f40f2bd\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3751171e elementor-widget elementor-widget-image\" data-id=\"3751171e\" 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\t<a href=\"https:\/\/fr.wikipedia.org\/wiki\/General_Atomics_MQ-9_Reaper\">\n\t\t\t\t\t\t\t<img decoding=\"async\" width=\"150\" height=\"150\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-14-150x150.png\" class=\"attachment-thumbnail size-thumbnail wp-image-170495\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-14-150x150.png 150w, https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-14-300x300.png 300w, https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-14.png 329w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\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>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-70828f5 e-flex e-con-boxed e-con\" data-id=\"70828f5\" 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<div class=\"elementor-element elementor-element-44ee296 e-con-full e-flex e-con\" data-id=\"44ee296\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-06dfdcb elementor-widget elementor-widget-text-editor\" data-id=\"06dfdcb\" 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>Stage 3: Image pre-processingStage 3: Image pre-processing<\/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-f1987ed elementor-widget elementor-widget-shortcode\" data-id=\"f1987ed\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/2d4c94833f521261a9aec7bdcf64d186.js\"><\/script><\/div>\n\t\t\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-ccf56b5 e-flex e-con-boxed e-con\" data-id=\"ccf56b5\" 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<div class=\"elementor-element elementor-element-3f79a8e e-con-full e-flex e-con\" data-id=\"3f79a8e\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b75ff93 elementor-widget elementor-widget-text-editor\" data-id=\"b75ff93\" 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>\u00c9tape 4 :<\/b><span style=\"font-weight: 400;\"> Pr\u00e9diction du mod\u00e8le<\/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-e61cda2 elementor-widget elementor-widget-shortcode\" data-id=\"e61cda2\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/1cf7fdd70574e0f61ff286dc00ed9c50.js\"><\/script><\/div>\n\t\t\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-17e6a7f e-flex e-con-boxed e-con\" data-id=\"17e6a7f\" 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<div class=\"elementor-element elementor-element-5b68bad7 e-con-full e-flex e-con\" data-id=\"5b68bad7\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-75f4dcaa elementor-widget elementor-widget-image\" data-id=\"75f4dcaa\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"592\" height=\"60\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/cnn-2.png\" class=\"attachment-full size-full wp-image-170496\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/cnn-2.png 592w, https:\/\/datascientest.com\/en\/files\/2023\/09\/cnn-2-300x30.png 300w\" sizes=\"(max-width: 592px) 100vw, 592px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Display of VGG16 model predictions<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\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-02811b4 e-flex e-con-boxed e-con\" data-id=\"02811b4\" 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<div class=\"elementor-element elementor-element-804a92b e-con-full e-flex e-con\" data-id=\"804a92b\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ed399dc elementor-widget elementor-widget-text-editor\" data-id=\"ed399dc\" 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 also display the 5 labels considered most likely by VGG16 :<\/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-0790527 elementor-widget elementor-widget-shortcode\" data-id=\"0790527\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/5c0c858cc543f0f3f4a9babc4149fa93.js\"><\/script>\n<\/div>\n\t\t\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-6884d94 e-flex e-con-boxed e-con\" data-id=\"6884d94\" 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<div class=\"elementor-element elementor-element-4fb3b761 e-con-full e-flex e-con\" data-id=\"4fb3b761\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-79b1f24f elementor-widget elementor-widget-image\" data-id=\"79b1f24f\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"226\" height=\"168\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/09\/CNN-15-.png\" class=\"attachment-full size-full wp-image-170497\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"> Top 5 labels considered most likely by VGG16<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\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-a63eb8d e-con-full e-flex e-con\" data-id=\"a63eb8d\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-74f49f1 elementor-widget elementor-widget-text-editor\" data-id=\"74f49f1\" 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>\u2192 VGG16 has successfully predicted with high confidence (95.6%) that not only the input image was a wolf but even went further by specifying its breed, which in this case is an Eastern Gray Wolf (timber wolf). Impressive, isn&#8217;t it?<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-23414f8 e-flex e-con-boxed e-con\" data-id=\"23414f8\" 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<div class=\"elementor-element elementor-element-de3d967 e-con-full e-flex e-con\" data-id=\"de3d967\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c30203b elementor-widget elementor-widget-text-editor\" data-id=\"c30203b\" 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>In the end, the operating principle of a CNN is quite easy to understand. However, paradoxically, implementing such a process to classify images remains very complex due to the considerable number of parameters to define: the number, size, movement of filters, choice of pooling method, the number of layers of neurons, the number of neurons per layer, and so on.<\/p><p>To overcome this obstacle, Python, through the Torchvision module, offers the possibility to use powerful pre-trained CNN models such as VGG16, Resnet101, etc.<\/p>\t\t\t\t\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-941eadb e-flex e-con-boxed e-con\" data-id=\"941eadb\" 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<div class=\"elementor-element elementor-element-7937697 e-con-full e-flex e-con\" data-id=\"7937697\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4b88286 elementor-widget elementor-widget-text-editor\" data-id=\"4b88286\" 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>In this article, we&#8217;ve explained the operation and architecture of Convolutional Neural Networks, with a focus on its specificity: the convolutional part.<\/p><p>We still have one more step in<a href=\"https:\/\/datascientest.com\/en\/management-of-unbalanced-classification-problems-i\"> classification<\/a> to cover: backpropagation of the error gradient, the famous gradient descent algorithm. Don&#8217;t worry, the next episode on this topic will be coming soon on the blog!<\/p>\t\t\t\t\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-70343c9 e-flex e-con-boxed e-con\" data-id=\"70343c9\" 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-0836329 elementor-widget elementor-widget-html\" data-id=\"0836329\" 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\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"Architecture of a Convolutional Neural Network-CNN\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"CNNs are a sub-category of neural networks and are currently one of the most powerful image classification models\".\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Example of a classical convolution filter\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"In the convolutional part of a Convolutional Neural Network, the input image passes through a succession of convolution filters. 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It is also interesting in that it reduces the computational cost by reducing the number of parameters to be learned and provides invariance through small translations (if a small translation does not modify the maximum of the region scanned, the maximum of each region will remain the same and so the new matrix created will remain identical).\"\n    }\n  }]\n}\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>In this section, we will focus on one of the most powerful algorithms in Deep Learning: Convolutional Neural Networks (CNNs). These are powerful programming models that enable image recognition, in particular, by automatically assigning a label to each image provided as input, corresponding to the class to which it belongs. Welcome to the third episode [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":170480,"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>Convolutional Neural Network: Everything You Need to Know<\/title>\n<meta name=\"description\" content=\"Convolutional Neural Network: Learn how CNNs work and their applications in the world of deep learning with the most...\" \/>\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\/convolutional-neural-network-everything-you-need-to-know\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Convolutional Neural Network: Everything You Need to Know\" \/>\n<meta property=\"og:description\" content=\"In this section, we will focus on one of the most powerful algorithms in Deep Learning: Convolutional Neural Networks (CNNs). 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