Hi all, I’m still a beginner with NN and was wondering if I could set up an extremely simple linear regression without any kind of interaction between features using PyTorch?  What I have in mind is illustrated below.  The idea is to obtain linreg coefficients and intercepts for each feature across tens of thousands of features and hundreds of thousands of samples.  This isn’t technically a NN, but the idea is to use the GPU and PyTorch infrastructure to accelerate acquisition of the coefficients / intercepts.
The closest I can find is an autoencoder, however here (a) there are no connections between different features, (b) there is only a single activation and no hidden layers, © the error is calculated by comparing the output with a different dataset with the same dimensions.
Thanks for any help and apologies if this is stupid 

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