How does Fine-tuning Work for Transfer Learning?

There are a lot of explanations elsewhere, here I’d like to share some example questions in an interview setting.

How does fine-tuning work for transfer learning?

Source: Transfer Learning Options: Feature-based vs. Fine-tuning

Here are some tips for readers’ reference:

Fine-tuning is a popular technique in transfer learning that involves taking a pre-trained neural network (usually trained on a large dataset like ImageNet) and then training it further on a smaller dataset that is related to the original dataset. The basic idea behind fine-tuning is to take advantage of the pre-trained network’s ability to extract meaningful features from the original dataset and use those features as a starting point for learning a new task.

Here’s how fine-tuning typically works:

• Take a pre-trained neural network that has already been trained on a large dataset, such as ImageNet.
• Replace the last layer(s) of the network with new layer(s) that are appropriate for the new task that you want to perform.
• Freeze the weights of all but the newly added layers to prevent them from being modified…