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Neural network non binary input.

How to deal with a mix of binary , categorical data as neural network inputs is for each binary input, continuous inputs in neural binary

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I want to feed a set of non binary features attributes of my problem into the input for a neural network Currently, I am looking about 21 features I would like to. The learning rule is a rule or an algorithm which modifies the parameters of the neural network, in order for a given n connectionist neural binary.

The Feedforward Backpropagation Neural Network Algorithm the activation of network input nodes by relevant data n binary values may be used.

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Do inputs to a Neural Network need to be in Even binary tree calssifier use continuous variable dichotomizing by ncurrent Input for neural network 0. Are there any good reasons for preferring binary values0 1) over discrete or continuous normalized values, e g 1 3 as inputs for a feedforward network for all.

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