👉 Transfer computing is a paradigm where knowledge or models developed for solving one problem are repurposed to address different but related challenges. This approach leverages pre-trained models, often developed for specific tasks like image recognition or natural language processing, and fine-tunes them on new datasets or different tasks. By transferring learned features and representations, transfer computing enables more efficient and effective solutions across various domains, reducing the need for extensive data collection and training from scratch. This not only accelerates innovation but also enhances performance, especially in scenarios where data is scarce or computational resources are limited.
