Regression using MXNet

Question

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?

Answer 1

Simple regression with L2 loss is featured in 2 famous tutorials - you can just pick any of those and customize the loss:

• In the D2L.ai book (used at many universities): https://d2l.ai/chapter_linear-networks/linear-regression-gluon.html
• In The Straight Dope (guide to the python API of MXNet, gluon). A lot of that guide went into D2L.ai: https://gluon.mxnet.io/chapter02_supervised-learning/linear-regression-gluon.html