Fine tuning

Fine-tuning is a process in machine learning where a pre-trained model is adjusted to better suit a specific task or dataset, often involving small changes to the model's parameters. This technique allows the model to leverage existing knowledge while adapting to new data, improving performance on specialized tasks without requiring extensive computational resources. Fine-tuning is particularly beneficial for developers and researchers who aim to enhance model accuracy and efficiency in domain-specific applications.

Fine tuning was discovered on April 15th 2025 and it currently has a search volume of 74K with a growth of +614%.