I was trying to perform a ridge regression with python. Matrices were:

`X: [32496, 42309]`

`y: [32496, 1]`

Since, I had to perform ~4000 of them, and optimise everything (i.e. optimise regularisation parameter for every regression) I had to do it pretty quickly. With sklearn, really useful python library for machine learning, takes a day for every regression. I tried also this other library, h2o4gpu, that it’s essentially a porting to GPU of sklearn: very nice, well done, incredibly fast, but for ridge regression there is a bug, and you cannot run a loop of regressions.

This is why I decided to use keras, a high-level deep learning framework. You can easily implement a simple regression and add reguralisation. I did an example code to explain that. In this code you don’t see a big difference in time, but in a real application there is! Result: I can obtain a good approximation within ~10/20 sec., instead of 1 day. 👍🏻

The cons are: you need a gpu, you need to install deep learning libraries, and the training can be quite tricky.

Take a look to the github page.