👉 Adaptation math, also known as adaptation learning or transfer learning, is a machine learning approach that leverages knowledge gained from solving one problem to improve performance on related but distinct problems. It involves adapting a pre-trained model, often developed for a specific task or dataset, to new tasks or datasets with limited data. This is achieved through techniques like fine-tuning, where the model's weights are adjusted on the new task's data, or feature extraction, where the pre-trained model's learned features are used as input for a new classifier. Adaptation math is particularly useful when labeled data for the new task is scarce, as it allows models to generalize better and achieve higher accuracy by building on existing knowledge.
