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r/MachineLearning - [D] What is the best neural network structure to approximate functions?

#artificialintelligenceOct-22-2018, 05:05:31 GMT

I have heard that both feed-forward networks as well as recurrent neural networks are universal function approximators, i.e., they can approximate any function arbitrarily close with increasing hidden layers and hidden units. Is there a difference between smooth and non-smooth functions? What is better, a few hidden layers with many hidden units per layer or deep learning, i.e., many hidden layers? Has anyone tried to use LSTM networks to do this kind of stuff?

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