👉 Adjustment engineering is a method used in machine learning and artificial intelligence to fine-tune pre-trained models for specific tasks or domains without starting from scratch. It involves taking a model that has already been trained on a large dataset and adjusting its parameters to better fit the nuances of a particular problem or dataset. This is typically done by adding a few more layers to the model, fine-tuning these layers with a smaller learning rate, and training them on a task-specific dataset. The goal is to leverage the pre-existing knowledge of the model while adapting it to perform more efficiently and accurately on the new task, often resulting in better performance with less computational cost compared to training a model from scratch.
