What is transfer learning in deep learning?

What is transfer learning in deep learning?

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.

How does it improve model performance?

It allows the model to leverage knowledge from a related task, reducing the amount of data and training time needed.

Can you give an example of transfer learning?

Sure! A common example is using a pre-trained image classification model like VGG16 and fine-tuning it for a specific image recognition task.

What are the benefits of using transfer learning?

Benefits include faster training, improved performance with less data, and the ability to tackle complex tasks more effectively.