What is transfer learning in AI?

What is transfer learning in AI?

Transfer learning is a technique where a model developed for a specific task is reused as the starting point for a model on a second task.

Can you give me an example of transfer learning?

Sure! A common example is using a pre-trained image recognition model, like ResNet, and fine-tuning it for a specific task, such as identifying different species of flowers.

Why is transfer learning useful?

It's useful because it allows us to leverage existing knowledge, reducing the amount of data and time needed to train a new model.

What are some challenges with transfer learning?

Some challenges include negative transfer, where the pre-trained model's knowledge does not apply well to the new task, and the need for careful selection of the source and target tasks.