Is this idea to calculate the required number of hidden neurons for a single hidden layer neural network correct?
How many nodes/hidden layers are required to solve a classification problem where the boundary is a sinusoidal function?
What could be the problem when a neural network with four hidden layers with the sigmoid activation function is not learning?
Are these statements about the performance of neural networks as a function of the number of hidden layers contradictory?
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