Mathematics of neural networks in machine learning: Difference between revisions

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* an ''activation function'' <math>f</math> that computes the new activation at a given time <math>t+1</math> from <math>a_j(t)</math>, <math>\theta_j</math> and the net input <math>p_j(t)</math> giving rise to the relation
 
:: <math> a_j(t+1) = f(a_j(t), p_j(t), \theta_j), </math>
 
* and an ''output function'' <math>f_{out}</math> computing the output from the activation
 
:: <math> o_j(t) = f_\text{out}(a_j(t)). </math>
 
Often the output function is simply the [[identity function]].