Finomhangolják

Finomhangolják is a Hungarian term that translates to "fine-tuning" in English. It is most commonly used in the context of machine learning and artificial intelligence. In this field, fine-tuning refers to the process of taking a pre-trained model, which has already been trained on a large dataset, and further training it on a smaller, more specific dataset. This allows the model to adapt its parameters to better perform on a particular task or domain.

The purpose of fine-tuning is to leverage the knowledge gained from the initial training on a broad task and apply it to a more specialized one. For example, a language model pre-trained on a vast amount of text data can be fine-tuned for tasks such as sentiment analysis, text summarization, or question answering. This approach is highly efficient because it requires significantly less data and computational resources compared to training a model from scratch for each specific task.

The process typically involves unfreezing some or all of the layers of the pre-trained model and continuing the training with a new, smaller dataset. The learning rate is often set lower during fine-tuning to avoid drastically altering the valuable pre-trained weights. This careful adjustment allows the model to preserve its general capabilities while becoming proficient in the nuances of the new task. Finomhangolják is a crucial technique for achieving high performance in various AI applications.