finomhangolások

Finomhangolások, often translated as fine-tuning, refers to a process in machine learning where a pre-trained model is further trained on a smaller, specific dataset. This technique is widely used in natural language processing and computer vision to adapt general-purpose models to specialized tasks. A pre-trained model, having learned a broad range of features from a large, diverse dataset, already possesses a foundational understanding of the domain. Finomhangolások leverages this existing knowledge by adjusting the model's parameters to better perform on a new, narrower task. For instance, a language model pre-trained on a vast corpus of text can be fine-tuned for sentiment analysis or question answering by training it on a dataset specifically curated for those tasks. Similarly, an image recognition model trained on millions of general images can be fine-tuned to identify specific types of medical scans. The advantage of finomhangolások lies in its efficiency; it requires significantly less data and computational resources compared to training a model from scratch. It also often leads to superior performance on the target task due to the model's ability to build upon its prior learning. The process typically involves unfreezing some or all of the pre-trained model's layers and training them with a lower learning rate on the new dataset. This careful adjustment ensures that the model adapts to the new task without forgetting the general knowledge acquired during pre-training.