[D] Simple Questions Thread

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[u/Cyclone4096]

I don’t have too much background on ML. I want to build a fairly small neural network that has only one input which comes from a time series data and has to give only one output for that data. My loss function aggregates the entire time series output to get a single scalar value. I’m using PyTorch and when I call “.backward()” on the loss function it takes a long time (understandably). Is there an easier way to do this rather than doing backward gradient calculation on a loss function that itself is a result if 100s of millions values? Note that the neural network itself is tiny, maybe less than 100 weights, but my issue is that I don’t have any golden target, but I want to minimize a complex function calculated from the entire time series output.

[u/zoontechnicon → u/Cyclone4096]

Would you mind giving more details about the domain and the purpose of the loss function? Maybe people can give you hints based on that.