Neural Network

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neunet 1 JCI neunet neucom JCR 2 2

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I understand neural networks with any number of hidden layers can approximate nonlinear functions however can it approximate f x x 2 I can t think of how it could It

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Here are some further illustrations showing the result of a simple 2 layer feed forward neural network with and without bias units on a two variable regression problem

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Many neural network training algorithms involve making multiple presentations of the entire data set to the neural network Often a single presentation of the entire data set is

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I am trying to implement neural network with RELU input layer 1 hidden layer relu output layer softmax layer Above is the architecture of my neural network I am

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Why do we have to normalize the input for a neural network I understand that sometimes when for example the input values are non numerical a certain transformation must be performed

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How can I convert my own dataset to be usable by pytorch geometric for a graph neural network All the tutorials use existing dataset already converted to be usable by

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4 I can remember I saw an example of Neural network trained with back propagation to approximate the probability of an outcome in the book Introduction to the theory

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I was trying to understand the difference between the use of a neural network with just one output neuron and one with multiple neurons in the output layers I know that with this type of neural n