I am reading a lot of articles about neural networks and I found very different information. I understand that the supervised neural network can be also regression and classification. In both cases I can use the sigmoid function but what is the difference?
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I would like to know what is the difference about using sigmoid. If the formula is the same what is the change? – Inuraghe Mar 11 '22 at 15:04
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A single-layer neural network is essentially the same thing as linear regression. That's because of how neural networks work: Each input gets weighted with a weight factor to produce an output, and the weight factors are iteratively chosen in such a way that the error (the discrepancy between the outputs produced by the model and the correct output that should be produced for a given input) is minimised. Linear regression does the same thing. But in a neural network, you can stack several of such layers on top of each other.
Classification is a potential, but by far not the only, use case for neural networks. Conversely, there are classification algorithms that don't use neural networks (e.g. K-nearest neighbours). The sigmoid function is often used as an activation function for the last layer in a classifier neural network.
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I can downvoting, mine reputation is low. I understand your answer but this isn't reply by question. – Inuraghe Mar 12 '22 at 09:10