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I'd like to ask for any kind of assistance regarding the following problem:

I was given the following training data: 100 numbers, each one is a parameter, they together define a number X(also given).This is one instance,I have 20 000 instances for training.Next, I have 5000 lines given, each containing the 100 numbers as parameters.My task is to predict the number X for these 5000 instances.

I am stuck because I only know of the sigmoid activation function so far, and I assume it's not suitable for cases like this where the output values aren't either 0 or 1.

So my question is this : What's a good choice for an activation function and how does one go about implementing a neural network for a problem such as this?

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Usually you're normalizing the data first, meaning that your whole dataset will be in between 0 and 1. Afterwords after you're having the model predictions, when computing the cost function or evaluating the model, you can apply the inverse of the normalization function.

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  • $\begingroup$ I'm a little confused now, the point of the sigmoid function is to put the data set between 0 and 1? $\endgroup$ – Grimnast Dec 11 '19 at 23:35
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    $\begingroup$ No, you use sigmoid to introduce non-linearity in your model. In addition sigmoid is especially used for models where we have to predict the probability as an output since it's defined in the range of 0 and 1. For normalizing the data i would suggest you to look at min-max normalization or z-score normalization. $\endgroup$ – razvanc92 Dec 12 '19 at 8:13

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