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For questions related to the concept of neural (network) architecture search (NAS), which is a way of automating the design (that is, the hyper-parameters) of a neural network. NAS is related to neuroevolution, given that neuroevolution can be used to perform NAS, but neuroevolution is not the only way of performing NAS. For example, reinforcement learning can also be used to perform NAS.
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Is it possible to train a neural network with 3 inputs and 12 outputs?
The perceptron convergence theorem states that any architecture will lead to a correlation between the data.
Yes, you can!