Glad it was useful!

That's great to hear! Thanks

Thanks!

Thank you, glad you liked it!

Thanks!

Glad it was useful!

Thanks!

Thanks!

Cheers!

Hi! Transfer learning is where a model trained on one task can be applied to another (usually similar task) while meta learning is generally trying to understand things like which parts of their data is most valuable / which approaches generate the best predictions on a given dataset (using machine learning techniques).

hi. Each support set is passed to it's respective copy of the model, and then an overall loss is calculated - this I find is easiest to see as a single function for the overall loss at 4.38 ... Now we can calculate the gradient of the overall loss wrt to the original parameters, because the initialisation of each model copy is with the original model parameters (which are tied/same across the copied models) -> i.e. we have a differentiable function from the original model parameters to the output loss which can be differentiated and thus we can do back propagation to calculate this gradient (and then update the model parameters)... hope this is helpful?

Hi, thanks! What do you exactly mean by edit formulation?

@@TwinEdProductions sorry,my english is bad,l just wonder the tool that you edit formulation.

@@calypsochris989 We write all mathematical formula using LaTex and convert to images using online tools like latex2png

Could you clarify what you mean by ensemble 'learning'? Do you mean training independent models with different seed initialisations and averaging their predictions?

@@TwinEdProductions yes, I thought about it more and read more about it and I realized that maml creates a generalized model that can be later used to learn more specific things later on, whereas in Ensemble learning (all kinds) we only train on the specific task.