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I've been reading about neural networks for a long time, and I saw that in each one, the weights are always between 0 and 1. Why is this? I tried programming one, but the sigmoid function just seemed like one more thing to make it more complicated, without need. Why couldn't the range of a weight or neuron value be infinite?

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Having the weights between 0 and 1 helps accelerate learning. They do not have to be between 0 and 1. Typically the weights get normalized to [-1, 1]. But it also depends on your problem.

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    $\begingroup$ Can you provide some evidence for the claim "Having the weights between 0 and 1 helps accelerate learning."? Have you observed this empirically? If yes, it might be a good idea to provide the link to such an experiment. If you have seen this written in a research paper or book, cite it. $\endgroup$
    – nbro
    Mar 5 at 9:05

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