In few-shot learning, are there gradient updates from the examples? If not, what difference does it make?

Amazing. This seems like a great way to learn how things are currently being done in ML

To me this seems more related to the early-stopping parameters. Important questions are:

1. What's the minimal percentage drop in validation loss you accept? If it's too high (20%), you don't train much. If it's too low (0.05%), it won't stop training.
2. What interval of validations are you using? If you check for earlystop every validation, an erratic loss may make the check inconsistent. If it takes too long to check again, the model may already be overfitting

oh wow it's really him, guys 😮

Is this actually Yannic? I read Meta's blog post about Cicero yesterday. I really like this video series. Will definitely watch it