👉 Transfer engineering is a process in machine learning and artificial intelligence where knowledge gained from solving one problem or performing one task is applied to a different but related problem or task. This approach leverages pre-trained models or learned features from extensive datasets to enhance performance on new, often smaller, datasets. By transferring learned representations or models, engineers can significantly reduce training time, improve model accuracy, and overcome data scarcity issues. This is particularly useful in scenarios where labeled data is limited or expensive to obtain, allowing for more efficient and effective model development across various domains such as natural language processing, computer vision, and robotics.
