hienosäätöprosessi

Hienosäätöprosessi, often translated as fine-tuning, refers to a machine learning technique where a pre-trained model is further trained on a smaller, specific dataset. This process adapts the general knowledge of the pre-trained model to a particular task or domain. Pre-trained models, typically trained on vast amounts of general data, have learned a wide range of features and patterns. Hienosäätö takes advantage of this learned knowledge, preventing the need to train a model from scratch for every new task.

During hienosäätö, the weights of the pre-trained model are adjusted based on the new dataset. This adjustment can involve updating all layers of the model or only a subset of the later layers, leaving the earlier layers that capture more general features untouched. The learning rate used during hienosäätö is usually lower than that used for initial pre-training to avoid drastically altering the already learned parameters.

The primary benefit of hienosäätö is its efficiency. It significantly reduces the amount of data and computational resources required compared to training a model from scratch. This makes it particularly useful in scenarios where large labeled datasets are scarce. Hienosäätö is widely employed in various fields, including natural language processing for tasks like sentiment analysis or text classification, and in computer vision for image recognition on specialized datasets. It allows for the development of high-performing models with less effort.