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Do Artificial Neural Network with non-linear activation only in the output layer follows linearity?

A non-linear layer is a matrix multiplication followed by an activation function. A linear layer is just a matrix multiplication. You have a matrix multiplication, then another matrix multiplication, ...
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Do Artificial Neural Network with non-linear activation only in the output layer follows linearity?

If you have a sigmoid activation in your output layer, and linearities before, you can interpret it as a logistic regression model for binary classification (if ...
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Do Artificial Neural Network with non-linear activation only in the output layer follows linearity?

You are speaking about generalized linear models (GLM)
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NEAT can't solve XOR completely

Very old thread, but i will try to answer anyway. The configuration seems fine. Try to implement a simple NE (without augmenting topologies) and check if it works. ...
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What can unsupervised learning actually be used for and how can humans interpret the outputs?

A good example is face recognition on your phone, or in face recognition systems in general. The way they work is pass in infromation and tighten the channel width for information throughput, ...
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