I have a regression model based on various independent features which eventually predict a value with a custom loss function. Somewhat similar to the link below.
https://www.evergreeninnovations.co/blog-quantile-loss-function-for-machine-learning/
The current model is built using Tensorflow library but now I want to use MXNet becuase of the speed and other advantages it provides. How to write a similar logic in MXNet with a custom loss function?
Simple regression with L2 loss is featured in 2 famous tutorials - you can just pick any of those and customize the loss:
gluon). A lot of that guide went into D2L.ai:
https://gluon.mxnet.io/chapter02_supervised-learning/linear-regression-gluon.html