👉 Tuning computing, also known as model fine-tuning or post-training, is a process in machine learning where an existing pre-trained model is further optimized for a specific task or domain by adjusting its parameters using a smaller, task-specific dataset. This technique leverages the general knowledge already encoded in the pre-trained model to improve its performance and accuracy on a particular problem, often requiring less data and computational resources compared to training a model from scratch. Tuning can involve various strategies, such as adjusting learning rates, adding task-specific layers, or modifying the model architecture, to better align the model's predictions with the nuances of the target task. This approach is particularly valuable in scenarios where labeled data for the specific task is scarce, enabling the model to achieve state-of-the-art performance with relative efficiency.
