peenhäälestuse

Peenhäälestuse, often translated as fine-tuning, is a machine learning technique where a pre-trained model is adapted for a specific task. This process involves taking a model that has already been trained on a large, general dataset and further training it on a smaller, task-specific dataset. The initial training allows the model to learn general features and patterns, while the fine-tuning phase adjusts these learned features to better suit the nuances of the new, more specialized task.

The core idea behind peenhäälestuse is that the knowledge gained from the initial broad training is transferable. Instead of training a new model from scratch, which can be computationally expensive and require vast amounts of data, peenhäälestuse leverages the existing learned representations. This is particularly effective in areas like natural language processing and computer vision, where large foundational models have demonstrated remarkable generalization capabilities.

During fine-tuning, the parameters of the pre-trained model are updated based on the performance on the new dataset. Often, only the final layers of the model are retrained, or the entire model is retrained with a much smaller learning rate to avoid drastically altering the already learned weights. This approach significantly reduces the data and computational resources needed for a new task, making advanced machine learning more accessible and efficient.