{"id":183288,"date":"2024-03-18T09:39:00","date_gmt":"2024-03-18T08:39:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=183288"},"modified":"2024-03-14T22:01:15","modified_gmt":"2024-03-14T21:01:15","slug":"fine-tuning-what-is-it-what-is-it-used-for-in-ai","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/fine-tuning-what-is-it-what-is-it-used-for-in-ai","title":{"rendered":"Fine Tuning: What is it? What is it used for in AI?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"183288\" class=\"elementor elementor-183288\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d971cca e-flex e-con-boxed e-con\" data-id=\"d971cca\" 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-81bade4 elementor-widget elementor-widget-heading\" data-id=\"81bade4\" 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\">Le fine-tuning est une technique permettant de sp\u00e9cialiser un mod\u00e8le pr\u00e9-entra\u00een\u00e9 de Machine Learning sur une t\u00e2che sp\u00e9cifique. D\u00e9couvrez tout ce qu\u2019il faut savoir sur cette technique au c\u0153ur de l\u2019intelligence artificielle !Fine-tuning is a technique for specializing a pre-trained Machine Learning model on a specific task. Find out all you need to know about this technique at the heart of artificial intelligence!<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0c0bfe6 elementor-widget elementor-widget-text-editor\" data-id=\"0c0bfe6\" 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><a href=\"https:\/\/datascientest.com\/en\/manifold-learning-what-is-this-machine-learning-method\">Machine Learning<\/a> is evolving fast, very fast. In recent years, the design of pre-trained models has been propelled to the center of technological advances. These models, trained on vast data sets, capture general knowledge that can be applied to a wide variety of tasks.<\/p><p>In the field of <a href=\"https:\/\/datascientest.com\/en\/nlp-training-become-an-nlp-pro-and-master-the-art-of-natural-language-processing\">natural language processing<\/a> (NLP), in particular, large language models (LLMs) are booming. They have opened the door to a wide range of applications, from language translation and sentiment analysis to intelligent chatbots like <a href=\"https:\/\/datascientest.com\/en\/chatgpt-how-does-this-nlp-algorithm-work\">ChatGPT<\/a>. However, to meet the specific needs of different applications, a technique for specializing the model has emerged as a must: fine-tuning.<\/p><p>Before discussing this approach and its many advantages in detail, let&#8217;s take a look at pre-tuned models.<\/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-613d771 e-flex e-con-boxed e-con\" data-id=\"613d771\" 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-d3d30e9 elementor-widget elementor-widget-heading\" data-id=\"d3d30e9\" 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\">BERT, GPT... what is a pre-trained model?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aa1715e elementor-widget elementor-widget-text-editor\" data-id=\"aa1715e\" 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>Derived from large datasets, pre-trained models are neural networks holding general knowledge that can be applied to a variety of tasks. Well-known examples include <strong>GPT, BERT and RoBERTa.<\/strong><\/p><p>Each of these models has its own specific characteristics. The different layers and activation functions used impact the way the model processes information, interprets and represents data.<\/p><p>Fine-tuning involves <strong>adjusting certain model parameters, such as learning rates<\/strong>, neuron weights and other hyper-parameters that can be modified to suit a specific task.<\/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-4b0b206 elementor-widget elementor-widget-image\" data-id=\"4b0b206\" 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\/12\/fine-tuning-1.jpg\" 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-a530a68 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"a530a68\" 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\">Learn all about pre-trained models<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f07ae30 e-flex e-con-boxed e-con\" data-id=\"f07ae30\" 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-b99353a elementor-widget elementor-widget-heading\" data-id=\"b99353a\" 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 Fine-Tuning?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ce35550 elementor-widget elementor-widget-text-editor\" data-id=\"ce35550\" 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>Unlike initial training,<\/strong> which requires massive datasets such as ImageNet, this refinement focuses on more restricted and specialized data.<\/p><p>It is an iterative process that aims to improve the model&#8217;s performance on a particular task, without losing the prior <strong>knowledge acquired during initial training.<\/strong><\/p><p>The central idea lies in the model&#8217;s ability to generalize to new domains while retaining its ability to specialize.<\/p><p><strong>Fine-tuning<\/strong> makes it possible to adjust the weights of connections between neurons so that they are better adapted to the new task without significantly disrupting pre-existing knowledge.<\/p><p>This approach has a wide range of applications in many fields. In Computer Vision, for example, a model pre-trained on a vast collection of images can be fine-tuned to detect specific objects in a particular context, such as autonomous vehicles or surveillance cameras.<\/p><p>As an example, a model pre-trained on generic imaging data can be fine-tuned for the detection of specific organs in medical images.<\/p><p>Similarly, in <strong>natural language processing,<\/strong> they can be refined for specific tasks such as classifying legal documents, detecting emotional tone in texts, or even machine translation adapted to professional jargon.<\/p><p>A model pre-trained on generic text data can thus be fine-tuned to<a href=\"https:\/\/datascientest.com\/en\/empowering-natural-language-processing-with-hugging-face-transformers-api\"> classify sentiments in comments on a company&#8217;s Facebook page<\/a>. These few examples demonstrate just how useful this approach can be for a company!<\/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-0277f93 e-flex e-con-boxed e-con\" data-id=\"0277f93\" 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-4497935 elementor-widget elementor-widget-heading\" data-id=\"4497935\" 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 stages in the Fine-Tuning process<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b10a228 elementor-widget elementor-widget-text-editor\" data-id=\"b10a228\" 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>Fine-tuning<\/strong> requires a methodical and precise approach. The process begins with data collection and preparation. They must be of high quality, specific to the target task, and representative of the real-life scenarios the model will face.<\/p><p>Data cleansing is also essential to eliminate errors, duplicates and inconsistencies. This formatting then facilitates refinement.<\/p><p>The second step is to choose the pre-trained model best suited to the specific task. For example, a model pre-trained for image recognition may be better suited to object detection than one pre-trained for <strong>NLP.<\/strong><\/p><p><strong>Before starting Fine-Tuning,<\/strong> we evaluate the initial performance of the selected model on the target task. This provides a baseline against which subsequent improvement can be measured.<\/p><p>Then it&#8217;s time to fine-tune hyper-parameters such as learning rate, number of iterations and batch size. This makes all the difference between model convergence and <a href=\"https:\/\/datascientest.com\/en\/overfitting-what-is-it-how-can-i-avoid-it\">over-fitting<\/a>.<\/p><p>Techniques such as random search, grid search and Bayesian optimization can be used to find the best combinations of hyperparameters.<\/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-ea639ef elementor-widget elementor-widget-image\" data-id=\"ea639ef\" 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\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/12\/fine-tuning-2.jpg\" 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-77d41e5 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"77d41e5\" 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\">Know how to use the Fine-Tuning process<\/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-7d20ae1 e-flex e-con-boxed e-con\" data-id=\"7d20ae1\" 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-0bcce72 elementor-widget elementor-widget-heading\" data-id=\"0bcce72\" 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\">Advanced refining strategies<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7de2c80 elementor-widget elementor-widget-text-editor\" data-id=\"7de2c80\" 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 simple <strong>parameter tuning,<\/strong> advanced strategies can further optimize model performance while avoiding potential pitfalls.<\/p><p>Learning transfer involves using the prior knowledge acquired by a model on one task to improve its <strong>performance on a similar task.<\/strong><\/p><p>The lower layers responsible for detecting general features are often retained, while the upper layers can be adjusted for the new task.<\/p><p>However, this transfer can lead to over-fitting if the training data is too specific. The use of regularization techniques such as dropout can mitigate this risk by introducing randomness into the learning process.<\/p><p>Another technique is<strong> progressive fine-tuning,<\/strong> which involves refining the model in several stages. We start with higher, more specific layers, before moving on to lower ones.<\/p><p>The aim?<\/p><p>To enable smoother adaptation and reduce the risk of losing crucial knowledge. Evaluating the model&#8217;s performance at <strong>each stage is essential to understanding its evolution.<\/strong><\/p><p>This enables us to detect signs of over- or under-fitting, and adjust the strategy accordingly. All these advanced strategies help optimize model specialization <strong>while minimizing risk.<\/strong><\/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-ae8c2d7 e-flex e-con-boxed e-con\" data-id=\"ae8c2d7\" 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-5c3216f elementor-widget elementor-widget-heading\" data-id=\"5c3216f\" 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 best Fine-Tuning tools and libraries<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-21c88ae elementor-widget elementor-widget-text-editor\" data-id=\"21c88ae\" 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 <strong>success of Fine-Tuning<\/strong> depends heavily on the tools and libraries available to facilitate the process. Among the most widely used frameworks is Google&#8217;s TensorFlow.<\/p><p>It offers advanced fine-tuning features such as <a href=\"https:\/\/datascientest.com\/en\/api-gateway-how-it-works-and-its-advantages\">API<\/a>s for learning transfer, or modules like TensorFlow Hub for ready-to-use pre-trained models.<\/p><p>Keras, a high-level interface for <a href=\"https:\/\/datascientest.com\/en\/step-by-step-guide-installing-tensorflow-with-anaconda-on-macos\">TensorFlow,<\/a> simplifies the fine-tuning process, particularly for less experienced users. Its modular approach allows easy customization of models.<\/p><p>PyTorch, on the other hand, offers considerable flexibility, making it easy to manipulate model layers and adjust hyperparameters. Its popularity among researchers makes it a preferred choice.<\/p><p>Among the resources available online, the Hugging Face platform also offers pre-trained models for various Machine Learning tasks and tools such as the Transformers library.<\/p><p>Visualization solutions such as TensorBoard for TensorFlow or TensorBoardX for <a href=\"https:\/\/datascientest.com\/en\/pytorch-all-about-this-framework\">PyTorch<\/a> facilitate real-time monitoring of model performance during Fine-Tuning.<\/p><p>Finally, the developer communities on GitHub and Stack Overflow are invaluable resources. They offer code examples, tutorials and discussions to help solve specific Fine-Tuning problems.<\/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-f2a1531 elementor-widget elementor-widget-image\" data-id=\"f2a1531\" 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\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/12\/fine-tuning-4.jpg\" 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-e3b0246 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"e3b0246\" 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\">Fine-tuning tools and libraries<\/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-713b003 e-flex e-con-boxed e-con\" data-id=\"713b003\" 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-40abf27 elementor-widget elementor-widget-heading\" data-id=\"40abf27\" 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 challenges of Fine-Tuning: difficulties to overcome<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2f45797 elementor-widget elementor-widget-text-editor\" data-id=\"2f45797\" 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>Understanding the potential obstacles to<strong> Fine-Tuning<\/strong> is crucial to implementing effective solutions and guaranteeing the success of pre-tuned model optimization.<\/p><p>One of the main risks is <a href=\"https:\/\/datascientest.com\/en\/overfitting-what-is-it-how-can-i-avoid-it\">over-fitting,<\/a> already mentioned in the previous chapter. This is a situation where the model adapts too much to the training data and fails to generalize appropriately to the new data.<\/p><p>This phenomenon can be mitigated by using control techniques such as dropout, batch normalization or other methods introducing control mechanisms.<\/p><p>Similarly, the creation of a separate validation set makes it possible to monitor model performance on data not used during <strong>Fine Tunin<\/strong>g. This helps to detect signs of<strong> over-fitting and adjust parameters accordingly.<\/strong><\/p><p>Another problem: when the classes in the training data are unbalanced, the model may show unfair preferences towards the majority class. The use of class weights can help balance the influence of different classes on the loss function, ensuring better generalization.<\/p><p>In addition<strong>, Fine Tuning<\/strong> can sometimes accentuate biases present in the original data. Diversification of data sources, the use of synthetic generation techniques, or the application of bias correction methods can help overcome this shortcoming.<\/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-3cef2cd e-flex e-con-boxed e-con\" data-id=\"3cef2cd\" 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-97665bb elementor-widget elementor-widget-heading\" data-id=\"97665bb\" 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\">Conclusion: fine-tuning artificial intelligence models<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f78c6ba elementor-widget elementor-widget-text-editor\" data-id=\"f78c6ba\" 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>By allowing <a href=\"https:\/\/datascientest.com\/en\/artificial-intelligence-engineer-a-complete-guide-to-the-profession\">AI models<\/a> to specialize on specific tasks, Fine-Tuning maximizes their performance. This technique is at the heart of the AI revolution, enabling the technology to be deployed in a huge variety of fields.<\/p><p>New developments are expected in this field in the future. Multi-task fine-tuning will enable pre-trained models to evolve into architectures capable of adapting to multiple tasks simultaneously, optimizing efficiency in real-world scenarios requiring diverse skills.<\/p><p>Likewise, methods could become more dynamic to allow continuous adjustment of models as new data becomes available. This will eliminate the need to start the whole process from scratch.<\/p><p>To master all the subtleties of <strong>Fine Tuning,<\/strong> you can choose <strong>DataScientest training courses<\/strong>. Through our various courses, you can quickly acquire real expertise in <a href=\"https:\/\/datascientest.com\/en\/xai-or-explainable-artificial-intelligence-what-is-it-about\">artificial intelligence<\/a>.<\/p><p>Our Data Scientist and Machine Learning Engineer courses will teach you how to program in Python, master DataViz tools and techniques, Machine Learning and Data Engineering, as well as integration and continuous deployment practices for ML models.<\/p><p>These courses can be completed by continuing education or intensive BootCamp, and lead to <strong>&#8220;Project Manager in Artificial Intelligence&#8221;<\/strong> certification from the Coll\u00e8ge de Paris, a certificate from Mines ParisTech PSL Executive Education and AWS Cloud Practitioner certification.<\/p><p>The Deep Learning course runs continuously over 10 weeks. It teaches you how to use the Keras and Tensorflow tools, and AI techniques such as Computer Vision and NLP.<\/p><p>Finally, to enable you to exploit the full potential of AIs such as <a href=\"https:\/\/datascientest.com\/en\/dall-e-a-source-of-inspiration-for-the-creative-professions\">DALL-E<\/a> and ChatGPT, our Prompt Engineering &amp; Generative AI training course extends over two days and will make you an expert in prompt writing and fine-tuning.<\/p><p>All our training courses take place entirely by distance learning via the web, and our organization is eligible for funding options. Discover DataScientest now!<\/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-d0377b6 elementor-widget elementor-widget-image\" data-id=\"d0377b6\" 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\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/12\/fine-tuning-3.jpg\" 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-e1fd0f8 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"e1fd0f8\" 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\">Start a Fine-Tuning training<\/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-40a85f6 elementor-widget elementor-widget-html\" data-id=\"40a85f6\" 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 Fine-Tuning?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Unlike initial training, which requires massive datasets like ImageNet, this fine-tuning focuses on smaller, specialized data. 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Diversifying data sources, using synthetic generation techniques, or applying bias correction methods can help overcome this shortcoming.\"\n    }\n  }]\n}\n<\/script>\n\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>Le fine-tuning est une technique permettant de sp\u00e9cialiser un mod\u00e8le pr\u00e9-entra\u00een\u00e9 de Machine Learning sur une t\u00e2che sp\u00e9cifique. D\u00e9couvrez tout ce qu\u2019il faut savoir sur cette technique au c\u0153ur de l\u2019intelligence artificielle !Fine-tuning is a technique for specializing a pre-trained Machine Learning model on a specific task. 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