Fine-tuning: adaptation of pre-trained models

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The Fine-Tuning (fine tuning) is an advanced technique in Machine Learning which allows models to be adapted pre-training to specific tasks. Instead of training a model from scratch, which requires large volumes of data and computational power, the Fine-Tuning reuses prior knowledge and adjusts it to new contexts.

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

What is Fine-Tuning at Machine Learning

The Fine-Tuning is the process of fitting a model pre-training to a new task through additional training with domain-specific data.

Difference between Fine-Tuning and Transfer Learning

The Fine-Tuning is an advanced form of Transfer Learning, but they have key differences:

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Pablo Blanco

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Fine-tuning: adaptation of pre-trained models

Table of contents

What is Fine-Tuning at Machine Learning

Difference between Fine-Tuning and Transfer Learning

Pablo Blanco

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Fine-tuning: adaptation of pre-trained models

Table of contents

What is Fine-Tuning at Machine Learning

Difference between Fine-Tuning and Transfer Learning

Pablo Blanco

Related articles

Hyper-automation

DCOM

Parseo

Web environment

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