{"id":168034,"date":"2023-05-11T16:46:15","date_gmt":"2023-05-11T15:46:15","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=168034"},"modified":"2023-10-30T19:00:35","modified_gmt":"2023-10-30T18:00:35","slug":"u-net-computer-visions-neural-network","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/u-net-computer-visions-neural-network","title":{"rendered":"U-NET: Computer Vision&#8217;s neural network"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"168034\" class=\"elementor elementor-168034\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-36ad17d e-flex e-con-boxed e-con\" data-id=\"36ad17d\" 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-7cbb0aa elementor-widget elementor-widget-heading\" data-id=\"7cbb0aa\" 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\">U-NET is a neural network model dedicated to Computer Vision tasks and more particularly to Semantic Segmentation problems. Discover all you need to know: presentation, functioning, architecture, advantages, training\u2026<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ca48999 elementor-widget elementor-widget-text-editor\" data-id=\"ca48999\" 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><span style=\"font-weight: 400;\">Artificial intelligence is a broad technology with many branches. <\/span><b>Computer Vision is one of these sub-categories.<\/b><\/p><p><span style=\"font-weight: 400;\">It is an interdisciplinary scientific field that aims to enable computers to<\/span><b> &#8220;understand&#8221; images and videos<\/b><span style=\"font-weight: 400;\">. The aim is to automate the tasks performed by the human visual system.<\/span><\/p><p><span style=\"font-weight: 400;\">Thanks <\/span>to<b>\u00a0<\/b><a href=\"https:\/\/datascientest.com\/en\/all-about-deep-learning\" target=\"_blank\" rel=\"noopener\">Deep Learning<\/a><b>,<\/b><span style=\"font-weight: 400;\"> a lot of progress has been made in the field of computer vision in recent years. <\/span><b>Machines are now able to compete<\/b><span style=\"font-weight: 400;\"> with human vision in certain situations.<\/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-2ca9aa2 e-flex e-con-boxed e-con\" data-id=\"2ca9aa2\" 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-cbd427d elementor-widget elementor-widget-heading\" data-id=\"cbd427d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">The different tasks of Computer Vision<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c69c23f elementor-widget elementor-widget-text-editor\" data-id=\"c69c23f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">There are various computer vision tasks. One of the most common applications is <\/span><b>image classification.<\/b><span style=\"font-weight: 400;\"> This involves letting the computer identify the main object in an image and assigning a label to classify the image.<\/span><\/p><p><span style=\"font-weight: 400;\">It is also possible to<\/span><b> let the computer locate the location of the object in the image<\/b><span style=\"font-weight: 400;\">. It does this by enclosing the object in a &#8220;Bounding Box&#8221; that can be identified by numerical parameters related to the edges of the image.<\/span><\/p><p><span style=\"font-weight: 400;\">Object classification is limited to one object per image. <\/span><b>Object detection<\/b><span style=\"font-weight: 400;\"> is more complex and requires the computer to detect and locate all the different objects within an image.<\/span><\/p><p><b>Semantic segmentation<\/b><span style=\"font-weight: 400;\"> involves labeling each pixel in an image with a class corresponding to what is being represented. This is also known as &#8220;dense prediction&#8221;, as each pixel must be predicted.<\/span><\/p><p><span style=\"font-weight: 400;\">Unlike other computer vision tasks, semantic segmentation does not just produce labels and bounding boxes. It generates <\/span><b>a high-resolution image,<\/b><span style=\"font-weight: 400;\"> in which each pixel is classified.<\/span><\/p><p><b>Instance segmentation <\/b><span style=\"font-weight: 400;\">goes even further, by classifying each instance of the same class separately. For example, if an image shows three dogs, each dog is an instance of the class &#8220;Dog&#8221;. Each will be classified separately, for example using different colours.<\/span><\/p><p><span style=\"font-weight: 400;\">Through these different tasks, <\/span><b>the computer &#8220;understands&#8221; the content of the images <\/b><span style=\"font-weight: 400;\">with an increasingly precise level of granularity. In this issue, we will focus on the semantic segmentation task.<\/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-e710dec e-flex e-con-boxed e-con\" data-id=\"e710dec\" 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-1bd4cbd elementor-widget elementor-widget-heading\" data-id=\"1bd4cbd\" 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\">Applications and use cases of Semantic Segmentation<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4fad9f6 elementor-widget elementor-widget-text-editor\" data-id=\"4fad9f6\" 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;\">Semantic segmentation is used for a wide variety of applications. <\/span><b>Autonomous vehicles,<\/b><span style=\"font-weight: 400;\"> for example, require perception, planning and execution in constantly changing environments.<\/span><\/p><p><span style=\"font-weight: 400;\">They also require high accuracy, as <\/span><b>safety must be foolproof on the road<\/b><span style=\"font-weight: 400;\">. Thanks to semantic segmentation, it is possible for unmanned cars to detect open spaces on lanes, road markings and traffic signs.<\/span><\/p><p><span style=\"font-weight: 400;\">This AI technique is also used for <\/span><b>medical diagnostics.<\/b><span style=\"font-weight: 400;\"> The machines can support the analyses carried out by radiologists in order to reduce the time needed to make diagnoses.<\/span><\/p><p><span style=\"font-weight: 400;\">Another use case is <\/span><b>satellite mapping<\/b><span style=\"font-weight: 400;\">, which is very important for monitoring deforestation areas or for urbanisation. Semantic segmentation allows distinguishing different types of land in an automated way. The detection of buildings and roads is also very useful for traffic management or urban planning.<\/span><\/p><p><span style=\"font-weight: 400;\">Finally, <\/span><b>precision agriculture robots <\/b><span style=\"font-weight: 400;\">can use semantic segmentation to distinguish plantations from weeds. This allows them to automate weed control using less herbicide.<\/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-35ba730 elementor-widget__width-initial elementor-widget elementor-widget-video\" data-id=\"35ba730\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=uiE56h5LyXc&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-video .elementor-widget-container{overflow:hidden;transform:translateZ(0)}.elementor-widget-video .elementor-wrapper{aspect-ratio:var(--video-aspect-ratio)}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{height:100%;width:100%;display:flex;border:none;background-color:#000}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-wrapper{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .elementor-open-inline .elementor-custom-embed-image-overlay{position:absolute;top:0;right:0;bottom:0;left:0;background-size:cover;background-position:50%}.elementor-widget-video .elementor-custom-embed-image-overlay{cursor:pointer;text-align:center}.elementor-widget-video .elementor-custom-embed-image-overlay:hover .elementor-custom-embed-play i{opacity:1}.elementor-widget-video .elementor-custom-embed-image-overlay img{display:block;width:100%;aspect-ratio:var(--video-aspect-ratio);-o-object-fit:cover;object-fit:cover;-o-object-position:center center;object-position:center center}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-custom-embed-image-overlay{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-custom-embed-image-overlay img{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .e-hosted-video .elementor-video{-o-object-fit:cover;object-fit:cover}.e-con-inner>.elementor-widget-video,.e-con>.elementor-widget-video{width:var(--container-widget-width);--flex-grow:var(--container-widget-flex-grow)}<\/style>\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4c0009d elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"4c0009d\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/deep-learning-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\">Take a Deep Learning course<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-13e62ec e-flex e-con-boxed e-con\" data-id=\"13e62ec\" 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-6d7976a elementor-widget elementor-widget-heading\" data-id=\"6d7976a\" 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 is U-NET?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-977981d elementor-widget elementor-widget-text-editor\" data-id=\"977981d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">There are <\/span><b>different methods to solve semantic segmentation problems<\/b><span style=\"font-weight: 400;\">. The traditional approaches are to detect points, lines or edges. It is also possible to rely on morphology, or to assemble clusters of pixels.\u00a0<\/span><\/p><p><a style=\"font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-weight: var( --e-global-typography-text-font-weight ); text-align: var(--text-align); background-color: #ffffff;\" href=\"https:\/\/datascientest.com\/en\/deep-neural-network-what-is-it-and-how-is-it-working\" target=\"_blank\" rel=\"noopener\">Deep learning convolutional neural networks<\/a><span style=\"font-weight: 400;\">\u00a0are now widely used. They allow us to <\/span><b>tackle more complex problems<\/b><span style=\"font-weight: 400;\"> through image segmentation.<\/span><\/p><p><span style=\"font-weight: 400;\">One of the <\/span><b>most widely used neural networks for image segmentation is U-NET<\/b><span style=\"font-weight: 400;\">. It is a Fully Convolutional Neural Network Model. This model was originally developed by Olaf Ronneberger, Phillip Fischer, and Thomas Brox in 2015 for medical image segmentation.<\/span><\/p><p><b>The U-NET architecture<\/b><span style=\"font-weight: 400;\"> is composed of two &#8220;paths&#8221;. The first is the contraction path, also called the encoder. It is used to capture the context of an image.<\/span><\/p><p><span style=\"font-weight: 400;\">It is in fact <\/span><b>an assembly of convolution layers<\/b><span style=\"font-weight: 400;\"> and &#8220;max pooling&#8221; layers allowing the creation of a feature map of an image and the reduction of its size to reduce the number of network parameters.<\/span><\/p><p><span style=\"font-weight: 400;\">The second path is the symmetric expansion path, <\/span><b>also known as the decoder<\/b><span style=\"font-weight: 400;\">. It also allows for accurate localisation through transposed convolution.<\/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-502b303 e-flex e-con-boxed e-con\" data-id=\"502b303\" 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-7c62a95 elementor-widget elementor-widget-heading\" data-id=\"7c62a95\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">The benefits of U-NET<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5963d4e elementor-widget elementor-widget-text-editor\" data-id=\"5963d4e\" 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 the field of Deep Learning, it is necessary to use <\/span><b>large data sets to train models.<\/b><span style=\"font-weight: 400;\"> It can be difficult to assemble such large volumes of data to solve an image classification problem, in terms of time, budget and hardware resources.\u00a0<\/span><\/p><p><b>Data labelling<\/b><span style=\"font-weight: 400;\"> also requires the expertise of several developers and engineers. This is particularly the case for highly specialised fields such as medical diagnostics.<\/span><\/p><p><span style=\"font-weight: 400;\">U-NET addresses these problems, as it is <\/span><b>effective even with a limited data set<\/b><span style=\"font-weight: 400;\">. It also offers higher accuracy than conventional models.<\/span><\/p><p><span style=\"font-weight: 400;\">A <\/span><b>conventional autoencoder architecture<\/b><span style=\"font-weight: 400;\"> reduces the size of the input information and then the next layers. Decoding then begins, the linear feature representation is learned, and the frame size gradually increases. At the end of this architecture, the output size is equal to the input size.<\/span><\/p><p><span style=\"font-weight: 400;\">Such an architecture is ideal for preserving the initial size. The <\/span><b>problem is that it compresses the input<\/b><span style=\"font-weight: 400;\"> in a linear way, which prevents the transmission of all the features.<\/span><\/p><p><span style=\"font-weight: 400;\">This is <\/span><b>where U-NET comes into its own<\/b><span style=\"font-weight: 400;\"> with its U-shaped architecture. The deconvolution is done on the decoder side, which avoids the bottleneck problem encountered with an auto-encoder architecture and thus avoids the loss of features.<\/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-5d606fc elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"5d606fc\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"533\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/05\/u-net-architecture.jpg\" class=\"attachment-large size-large wp-image-168074\" alt=\"u-net-architecture\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/05\/u-net-architecture.jpg 1024w, https:\/\/datascientest.com\/en\/files\/2023\/05\/u-net-architecture-300x200.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/05\/u-net-architecture-768x512.jpg 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2963018 e-flex e-con-boxed e-con\" data-id=\"2963018\" 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-71cba8d elementor-widget elementor-widget-heading\" data-id=\"71cba8d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How to learn to use U-NET?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ce953f2 elementor-widget elementor-widget-text-editor\" data-id=\"ce953f2\" 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;\">As AI and Computer Vision are increasingly exploited in all sectors, mastering Deep Learning and various models like U-NET is a <\/span><b>valuable and coveted skill.<\/b><\/p><p><span style=\"font-weight: 400;\">To acquire it, you can turn to\u00a0<\/span><a href=\"https:\/\/datascientest.com\/en\/data-courses\" target=\"_blank\" rel=\"noopener\">DataScientest training courses<\/a><span style=\"font-weight: 400;\">. Machine Learning and Deep Learning are at the <\/span><b>heart of our Data Scientist course<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Through this course you will also learn Python programming, DataViz, and the use of databases and Big Data tools. At the end of the course, you will have<\/span><b> all the necessary skills <\/b><span style=\"font-weight: 400;\">to work as a data scientist in a fast-growing profession.<\/span><\/p><p><span style=\"font-weight: 400;\">Our professional training courses are designed to meet the real needs of companies and organisations. They can be done as a <\/span><b>BootCamp, or in continuous format<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">We offer an<\/span><b> innovative Blended Learning approach,<\/b><span style=\"font-weight: 400;\"> combining. At the end of the programme, you will receive a diploma certified by the Sorbonne University.<\/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-4cae0d7 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"4cae0d7\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Start a Data Scientist course<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-8a82271 e-flex e-con-boxed e-con\" data-id=\"8a82271\" 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-80736a8 elementor-widget elementor-widget-html\" data-id=\"80736a8\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"The different tasks of Computer Vision\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"There are various computer vision tasks. 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