{"id":179417,"date":"2024-05-07T11:36:34","date_gmt":"2024-05-07T10:36:34","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=179417"},"modified":"2024-02-22T14:42:14","modified_gmt":"2024-02-22T13:42:14","slug":"data-augmentation-what-is-it-whats-it-for","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/data-augmentation-what-is-it-whats-it-for","title":{"rendered":"Data augmentation: What is it? What&#8217;s it for?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"179417\" class=\"elementor elementor-179417\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-83841d7 e-flex e-con-boxed e-con\" data-id=\"83841d7\" 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-b10f970 elementor-widget elementor-widget-heading\" data-id=\"b10f970\" 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\">The accuracy and efficiency of Deep Learning depend largely on the quality and quantity of the training data. And even if we are fully in the era of Big Data, the quantity of information available is sometimes insufficient for building deep learning models. This is where data augmentation comes in. So what is it? How does it work? What are the advantages and disadvantages? That's what we'll be looking at in this article;<\/h2>\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-2d586b3 e-flex e-con-boxed e-con\" data-id=\"2d586b3\" 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-7cf929d elementor-widget elementor-widget-heading\" data-id=\"7cf929d\" 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 Data Augmentation?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1c4953f elementor-widget elementor-widget-text-editor\" data-id=\"1c4953f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><strong>Data augmentation<\/strong> makes it possible to artificially increase the amount of data used by <a href=\"https:\/\/datascientest.com\/en\/unraveling-machine-learning-vs-deep-learning-key-differences-explained\">Deep Learning tools<\/a>. The idea is to generate new data points from existing data, either by making minor modifications to the data, or even by using other machine learning models to amplify the data set.<\/p><p>As such, it&#8217;s important to distinguish between :<\/p><ul><li><strong>Synthetic data:<\/strong> this is data generated artificially without reference to the real world. In most cases, they are produced by generative adverbial networks.<\/li><li><strong>Augmented data:<\/strong> this comes from original data, to which minor transformations have been added (such as translating textual data into another language, rotating an image or adding noise to a video). These transformations increase the diversity of the learning set.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-acc3539 elementor-widget elementor-widget-text-editor\" data-id=\"acc3539\" 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 many cases,<strong> augmented data<\/strong> is the preferred choice for organizations, as it is more closely resembling reality. That said, in some cases, synthetic data may prove more relevant. Particularly <a href=\"https:\/\/datascientest.com\/en\/understanding-gdpr-definition-and-its-crucial-impact-on-businesses\">when it comes to complying with the GDPR (more on this later).<\/a><\/p><p>Today, data augmentation methods are widely used in <a href=\"https:\/\/datascientest.com\/en\/all-about-deep-learning\">Deep Learning applications<\/a>. For example, for object detection, image classification, image recognition, <a href=\"https:\/\/datascientest.com\/en\/empowering-natural-language-processing-with-hugging-face-transformers-api\">natural language understanding<\/a>, semantic segmentation and so on.<\/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-61204a9 e-flex e-con-boxed e-con\" data-id=\"61204a9\" 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-c0042d8 elementor-widget elementor-widget-heading\" data-id=\"c0042d8\" 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 does data augmentation work?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8da39c6 elementor-widget elementor-widget-text-editor\" data-id=\"8da39c6\" 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>To tell the difference between a cat, a dog, a horse or a dolphin, a Deep Learning model needs a multitude of photos representing these different animals. Above all, it needs a variety of images. That is, with different orientations, locations, scales, luminosities&#8230; It&#8217;s only when it&#8217;s able to accurately classify these different animals, whatever their orientation, size or lighting, that it&#8217;s truly operational. This is known as a <strong>convolutional neural network (CNN).<\/strong><\/p><p>It is on this conclusion that the use of data augmentation is based. The idea is to manipulate data by adding, deleting or modifying various parameters, in order to provide the learning model with a highly varied set of training data. The more variables the dataset offers, the more the <strong>CNN is able to learn complex differentiation features.<\/strong><\/p><p>So, in order to offer as many parameters as possible, the process of <strong>data augmentation<\/strong> begins. That said, the<strong> data augmentation<\/strong> technique varies according to the type of data used.<\/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-95a90fb elementor-widget elementor-widget-image\" data-id=\"95a90fb\" 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<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/07\/data-augmentation-cnn.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/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-2fd58e6 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"2fd58e6\" 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-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\">Master how data augmentation works<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f610de9 elementor-widget elementor-widget-heading\" data-id=\"f610de9\" 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\">For visual data<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d812be0 elementor-widget elementor-widget-text-editor\" data-id=\"d812be0\" 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>This is where the <strong>data augmentation<\/strong> process is at its simplest. Here are the steps to be implemented:<\/p><ul><li>Introduction of <a href=\"https:\/\/datascientest.com\/en\/all-about-data-pipeline\">input data into the data enhancement pipeline;<\/a><\/li><li>Implementation of sequential steps for various augmentations, such as rotation, color modification (from grayscale to RGB),<\/li><li>blurring and flipping (vertical and horizontal).<\/li><li><a href=\"https:\/\/datascientest.com\/en\/image-processing-fundamental-principles-and-practical-uses\">Image processing<\/a> at each sequential step, with assignment of a probability;<\/li><li>Random verification of augmented results by a human;<\/li><li>Use of augmented data in the AI training process.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bd8a0b2 elementor-widget elementor-widget-heading\" data-id=\"bd8a0b2\" 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\">For text data<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-97b089e elementor-widget elementor-widget-text-editor\" data-id=\"97b089e\" 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>Due to the complexity of <a href=\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\">natural language<\/a>, data augmentation is rarer in the <a href=\"https:\/\/datascientest.com\/en\/nlp-training-become-an-nlp-pro-and-master-the-art-of-natural-language-processing\">NLP field.<\/a> That said, even if it&#8217;s more difficult, it&#8217;s not impossible to artificially enrich textual data. Here&#8217;s how the data augmentation process works:<\/p><ul><li><strong>Easy data augmentation:<\/strong> for example, by replacing synonyms, inserting, swapping and deleting words.<\/li><li><strong>Back-translation<\/strong>: translated text from the target language is back-translated into its original language.<\/li><li><strong>Contextualized word embeddings:<\/strong> the idea here is to create relationships between words and sentences.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7755bc7 e-flex e-con-boxed e-con\" data-id=\"7755bc7\" 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-d63bb41 elementor-widget elementor-widget-heading\" data-id=\"d63bb41\" 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 advantages and disadvantages?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e59f780 elementor-widget elementor-widget-heading\" data-id=\"e59f780\" 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\">Advantages<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-12ad1a4 elementor-widget elementor-widget-text-editor\" data-id=\"12ad1a4\" 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>Data augmentation<\/strong> is an inexpensive and effective method for overcoming many problems in the<a href=\"https:\/\/datascientest.com\/en\/all-about-deep-learning\"> design of Deep Learning neural networks.<\/a><\/p><p>On the one hand, organizations are traditionally dependent on the process of collecting and preparing data.<\/p><p>Indeed, to <a href=\"https:\/\/datascientest.com\/en\/google-announces-new-ai-models-for-google-cloud\">build high-precision AI models<\/a>, they need large quantities of qualitative data. But while data collection and preparation are indispensable to Deep Learning, this step is extremely time-consuming and costly. Conversely, data augmentation enables large quantities of qualitative <strong>data to be obtained in record time.<\/strong><\/p><p>On the other hand, when companies collect and use personal data, they must comply with privacy regulations. This can limit the amount of data available and exploitable. Here, the generation of synthetic data makes it possible to obtain the volumes of data required, without infringing on individuals&#8217; right to privacy.<\/p><p>Above all, by <a href=\"https:\/\/datascientest.com\/en\/google-unveils-palm-2-its-revolutionary-ai-model\">generating new data artificially,<\/a> data augmentation enables deep learning models to use larger and more complete training datasets. This greatly improves their performance and the relevance of the results obtained.<\/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-147657c elementor-widget elementor-widget-heading\" data-id=\"147657c\" 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\">Disadvantages<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-59f93eb elementor-widget elementor-widget-text-editor\" data-id=\"59f93eb\" 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>Despite all the benefits of data augmentation, it is important to be aware of the limitations of this method of data enrichment:<\/p><ul><li>The biases inherent in original data persist in augmented data, and may even be reinforced.<\/li><li>Guaranteeing the quality of artificially augmented data sets also comes at a cost.<\/li><li>The creation of synthetic data requires significant resources (skills, advanced applications, research and development, etc.).<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3fdc31c e-flex e-con-boxed e-con\" data-id=\"3fdc31c\" 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-1fb3481 elementor-widget elementor-widget-heading\" data-id=\"1fb3481\" 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\">Deep Learning training with DataScientest<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4b2477d elementor-widget elementor-widget-text-editor\" data-id=\"4b2477d\" 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>Beyond data augmentation, the design of Deep Learning models requires a multitude of advanced technical skills (programming language, data engineering, AI, data visualization, &#8230;). This is precisely why Datascientest offers our Deep Learning training.<\/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-5a99441 elementor-widget__width-initial elementor-widget elementor-widget-video\" data-id=\"5a99441\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/kNPe_pgbuHg&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-80291f2 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"80291f2\" 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\/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\">Start 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-c4b5e51 e-flex e-con-boxed e-con\" data-id=\"c4b5e51\" 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-1c9499f elementor-widget elementor-widget-html\" data-id=\"1c9499f\" 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\": \"What is Data Augmentation?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Data augmentation artificially increases the amount of data used by Deep Learning tools. The idea is then to generate new data points from existing data, whether by making minor modifications to the data, or even by using other machine learning models in charge of amplifying the dataset.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"How does data augmentation work?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"To tell the difference between a cat, a dog, a horse or a dolphin, a Deep Learning model needs a multitude of photos representing these different animals. Above all, it needs a variety of images. That is, with different orientations, locations, scales, luminosities... It's only when it's able to accurately classify these different animals, whatever their orientation, size or lighting, that it's truly operational. This is known as a convolutional neural network (CNN).\"\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>The accuracy and efficiency of Deep Learning depend largely on the quality and quantity of the training data. And even if we are fully in the era of Big Data, the quantity of information available is sometimes insufficient for building deep learning models. This is where data augmentation comes in. So what is it? How [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":179419,"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>Data augmentation: What is it? 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