Image Categorization
As illustrated in the previous section, transfer learning helps detect and recognize objects in images. As a result, it makes image categorization a lot easier and faster. You no longer have to train a model to classify images since it’s already pre-trained.
The process is beneficial in several sectors, most notably in the medical field. Medical imaging apps no longer have to be trained from scratch and can immediately detect abnormalities such as kidney stones in scans.
2. Gaming
The application of AI in gaming has been explored as early as the 1990s. Different machines have defeated human game masters. We’ve seen IBM’s Deep Blue for chess, Chinook for checkers, and AlphaGo for the classic board game “Go.” These inventions, however, would not be able to win other matches. For example, if you use DeepMind’s AlphaGo to play chess, it would fail because it uses the traditional method of learning.
With the help of transfer learning, though, Deepmind developed another system that can use the strategies it learned from playing “Go” to play a different game.