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Looking at the variable names in the code and considering the context, it seems to me that the author is using the word "Lazy" to describe the approach. I believe the author actually means that a naïve approach to producing a grayscale image is being used. If you want to use a less-naïve approach, you might consider producing the dot product of ...


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First, I have not read and do not have that book. That said, I would interpret that statement in the context of the intractability of guaranteeing that the optimization function will find global minima in the loss surface. In other words, higher precision values will do nothing to improve whether we have descended into a global or local minimum. On the ...


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As DKDK said, Indeed one could fit both linear and exponential function and see which one has smaller residual, without using any complex AI. But OTOH this could be a great toy-problem for learning about neural networks. You could have a network with these parts: A network with a final sigmoid activation, which predicts whether the function is linear or not....


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How about dividing the problem? You can first train a classification model that predicts the type of function (linear or exponential). Then you can use your seperately trained nn depending on the classification output. P.S. I'm not sure why you would use a neural network for this problem. Fitting a linear/exponential function seems to be a relatively simple ...


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