{"id":3333,"date":"2025-10-06T16:10:44","date_gmt":"2025-10-06T14:10:44","guid":{"rendered":"https:\/\/tecnologia.euroinnova.com\/fine-tuning-adaptacion-de-modelos-preentrenados\/"},"modified":"2025-10-07T14:50:23","modified_gmt":"2025-10-07T12:50:23","slug":"fine-tuning","status":"publish","type":"post","link":"https:\/\/tecnologia.euroinnova.com\/en\/fine-tuning","title":{"rendered":"Fine-tuning: adaptation of pre-trained models"},"content":{"rendered":"
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The Fine-<\/strong><\/span>Tuning<\/strong><\/span> (fine tuning) is an advanced technique in Machine <\/strong><\/span>Learning<\/strong><\/span> which allows models to be adapted <\/span>pre-training<\/span> to specific tasks. Instead of training a model from scratch, which requires large volumes of data and computational power, the Fine-<\/strong><\/span>Tuning<\/strong><\/span> reuses prior knowledge and adjusts it to new contexts. <\/span> <\/span><\/p>\n<\/div>\n

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Today, this approach is widely used in applications such as natural language processing (NLP)<\/strong>, computer vision<\/strong> and more. Thanks to <\/span>frameworks<\/span> as <\/span>TensorFlow<\/strong><\/span>, <\/strong><\/span>PyTorch<\/strong><\/span> y <\/strong><\/span>Hugging<\/strong><\/span> <\/strong><\/span>Face<\/strong><\/span>, the Fine-<\/span>Tuning<\/span> has become an accessible tool for developers and data scientists. <\/span> <\/span><\/p>\n<\/div>\n

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What is Fine-<\/strong><\/span>Tuning<\/strong><\/span> at Machine <\/strong><\/span>Learning<\/strong><\/span> <\/span><\/span><\/h2>\n<\/div>\n
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The Fine-<\/strong><\/span>Tuning<\/strong><\/span> is the process of fitting a model <\/span>pre-training<\/span> to a new task through additional training with domain-specific data. <\/span> <\/span><\/p>\n<\/div>\n

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Difference between Fine-<\/strong><\/span>Tuning<\/strong><\/span> and Transfer <\/strong><\/span>Learning<\/strong><\/span> <\/span><\/span><\/h3>\n<\/div>\n
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The Fine-<\/strong><\/span>Tuning<\/strong><\/span> is an advanced form of Transfer <\/strong><\/span>Learning<\/strong><\/span>, but they have key differences: <\/span> <\/span><\/p>\n<\/div>\n<\/div>\n

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