When should I use transfer learning?

What is transfer learning and when should I use it?

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. You should use it when you have limited data for the new task but a large dataset for a related task.

Can you give me an example of when transfer learning is beneficial?

Sure! For instance, if you're building a model to classify images of cats and dogs but only have a few hundred images, you can use a pre-trained model like VGG16, which was trained on millions of images, to improve your model's performance.

Are there any downsides to using transfer learning?

Yes, one downside is that if the source and target tasks are too different, the pre-trained model may not perform well. Additionally, it can lead to overfitting if not properly fine-tuned.

How do I fine-tune a pre-trained model for my specific task?

Fine-tuning involves training the pre-trained model on your specific dataset for a few epochs, adjusting the learning rate, and possibly freezing some layers to retain learned features.