The important characteristic of the activation function is that it provides a smooth transition as input values change, i . e . a small change in input produces a small change in output.
42.
Kurt Hornik showed in 1991 that it is not the specific choice of the activation function, but rather the multilayer feedforward architecture itself which gives neural networks the potential of being universal approximators.
43.
Where is the output of the th neuron, is the th input neuron signal, is the synaptic weight ( or strength of connection ) between the neurons and, and is the activation function.
44.
Two functions have been identified in AR that have critical roles in the regulation of target gene transactivation, the N-terminal activation function 1 ( AF1 ) and the C-terminal activation function 2 ( AF2 ).
45.
Two functions have been identified in AR that have critical roles in the regulation of target gene transactivation, the N-terminal activation function 1 ( AF1 ) and the C-terminal activation function 2 ( AF2 ).
46.
In order for the hidden layer to serve any useful function, multilayer networks must have non-linear activation functions for the multiple layers : a multilayer network using only linear activation functions is equivalent to some single layer, linear network.
47.
In order for the hidden layer to serve any useful function, multilayer networks must have non-linear activation functions for the multiple layers : a multilayer network using only linear activation functions is equivalent to some single layer, linear network.
48.
The protein encoded by this gene mediates transcriptional control by interaction with the activation function 2 ( AF2 ) region of several nuclear receptors, including the alternatively spliced transcript variants encoding different isoforms have been described for this gene.
49.
This would therefore create the Hopfield dynamical rule and with this, Hopfield was able to show that with the nonlinear activation function, the dynamical rule will always modify the values of the state vector in the direction of one of the stored patterns.
50.
By assigning a softmax activation function, a generalization of the logistic function, on the output layer of the neural network ( or a softmax component in a component-based neural network ) for categorical target variables, the outputs can be interpreted as posterior probabilities.
