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I presume when you say input you may be referring to the target values (the things you are trying to predict). If not, then some parts of your question might not make sense, like your proposal to apply a scaling. In any case I would consider what the target distribution is before using a sigmoid and applying a scaling. The thing about a sigmoid is that the ...

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There are several functions that can be denoted as sigmoid functions, such as the logistic function and the hyperbolic tangent, given that they have an $S$-shaped curve. You can find more info about them in the related Wikipedia article. However, when people use the term sigmoid function, they typically refer to the logistic function, which is a function of ...

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I think the author refers to both different choices of activation function and loss. It is explained in more detail in chapter 2. In particular 2.3 is ilustrative of this point. I don't think there is a relation between this argument and universal approximation theorems, which state that certain classes of neural networks can approximate any function in ...

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ReLU is piecewise linear function that outputs the received input directly if it's positive, or outputs a zero. i.e., $max(0, x)$ How significant is adding relu to full connected layers? ReLU, being an activation function, will determine what the output of the nodes in your FCs are. Since it's a non-linear function, one significance is it will allow the ...

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The problem was due to the following issues in my implementation: The offspring generated in the crossover was not mutated (!) The mutations did not occur with the expected frequencies (too few links and weight mutations) The sigmoid activation had to be steepened Another thing that previously caused issues was the network.activate function. Make sure that ...

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