Transfer learning is a smart trick for computers. It means computers use what they learned from one task to do better at a similar new task. For example, a computer knowing cars can quickly learn to spot trucks. This helps computers learn much faster and more efficiently.
This idea is actually quite old. People first talked about it for computer networks in 1976. By the 1980s, computers used it to recognize images like letters. Experts like Andrew Ng believed it would be very important. However, sometimes starting fresh might be a better approach for accuracy.
Transfer learning is useful in many different areas. It helps computers identify types of cancer or sort text. It can also filter out spam emails. A cool discovery in 2020 showed it works with muscle and brain signals. This helps computers learn from different experiences and knowledge.
