sorry if this is too much of an open question, but I'm looking for resources on the theory behind choosing a loss function for ML---I'm interested in GBDT but for deep learning would work as well. I'd like to get a better understanding of how the loss function affects the model, the difference between validation loss and training loss, etc.

I've google around and most medium articles I find are too superficial, and I don't know where to start looking for more academic resources.

Thanks!

I'm looking for resources on the theory behind choosing a loss function for ML---I'm interested in GBDT but for deep learning would work as well. I'd like to get a better understanding of how the loss function affects the model, the difference between validation loss and training loss, etc.

I've googled around and most medium articles I find are too superficial, and I don't know where to start looking for more academic resources.