Transfer learning involves utilizing knowledge gained from one task to improve performance on a different, yet related task. Samory highlights the importance of clear definitions, noting that data distribution shifts can complicate the application of models, as illustrated by the challenges faced by voice assistants like Siri when adapting to diverse accents. The conversation emphasizes the need for more representative data to enhance model effectiveness across varying contexts.
