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I'm working on implementation of artificial neuron which be extended to neural net. I want do implementation by myself to fully understand how it works. I start with perceptron with threshold activation function:

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and then questions come along

  • can implementaion be change to operate with more than 2 classes?
  • if I build network with few layers, first (and nexts) layers should return predicted class or value from first layer?
  • Is better artificial neuron to build neural net than perceptron?
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  • The implementation can be changed for more than two classes. You can either use two neurons or you can use the following structure: $$ f(x)=\left\{\array{2 & \textrm{if} \;wx+b>0\\ 1 & \textrm{if} \; wx+b\leq 0 \; \textrm{and}\;ux+c>0\\ 0 & \textrm{otherwise} \\ }\right. $$ where $u$ is of the same size as $w$ and $c$ is a scalar.
  • Typically, only the output layers are used.
  • The described neuron is better in terms of interpretation but is difficult to be trained by gradient-based algorithms such as backpropagation.
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