Fine-tuning is a technique of refining a pre-trained model on a specific dataset. It helps in achieving better accuracy and performance on new data.

The pre-trained model is used as a starting point and then further trained on new data, allowing it to adapt to the specific task at hand.

Fine-tuning allows for faster and more efficient training on new data, reducing the need for extensive training from scratch.

Finetuning is widely used in various applications such as natural language processing, image recognition, and audio analysis.

Finetuning can improve the performance of machine learning models, reduce training time, and require less data to achieve good results.

Finetuning requires a good understanding of the underlying model and its architecture, as well as the specific dataset being used.

Some best practices for finetuning includes
 
– choosing the right pre-trained model– selecting an appropriate dataset

Finetuning is a powerful technique that can unleash the true potential of machine learning, enabling models to adapt to specific tasks with greater accuracy and efficiency.