Training Loss and Validation Loss in Deep Learning

Question

Would you please guide me how to interpret the following results?

1) loss < validation_loss 2) loss > validation_loss

It seems that the training loss always s

Answer 1

Really a fundamental question in machine learning.

If validation loss >> training loss you can call it overfitting.
If validation loss  > training loss you can call it some overfitting.
If validation loss  < training loss you can call it some underfitting.
If validation loss << training loss you can call it underfitting.

Your aim is to make the validation loss as low as possible. Some overfitting is nearly always a good thing. All that matters in the end is: is the validation loss as low as you can get it.

This often occurs when the training loss is quite a bit lower.