Why The Sigmoid Function Is significant In Neural Networks?

 


Activation Functions 

 All activation functions must exist bounded, uninterrupted, monotonic, and continuously differentiable with reference to the weights for optimization objectives. The most generally utilized activation function is the sigmoid function. Other attainable activations are the curve- divagation function and the hyperbolic- tangent function. Activation function decides, whether a neuron should be actuated or not by computing weighted sum and added adding bias with it. 


 Why The Sigmoid Function Is significant In Neural Networks? 

 




 Still, also this model can only learn linearly divisible problems, If we apply a linear activation function in a neural network. Still, with the addition of just one hidden level and a sigmoid activation function in the hidden level, the neural network can easy learn anon-linearly divisible problem. applying anon-linear function produces non-linear extents and hence, the sigmoid function can be applied in neural networks for getting complicate decision functions. 


 The only non-linear function that can be applied as an activation function in a neural network is one which is monotonically amplifying. The function is also necessitated to be differentiable over the whole space of real figures. 

 

 Generally a back propagation algorithm uses grade descent to get the weights of a neural network. To reason this algorithm, the by-product of the activation function is needed. The fact that the sigmoid function is monotonic, continued and differentiable everyplace, coupled with the property that its derivate can be raised in terms of itself, makes it effortless to decide the update equations for learning the weights in a neural network when operating back propagation algorithm. 

Learn what is sigmoid in details .

 Uses

Generally applied in affair subcaste of a double bracket, where result is either 0 or 1, as value for sigmoid function lies between 0 and 1 only so, result can be prognosticated fluently to be 1 if value is lesser than0.5 and 0 else. 



Conclusion 

So, here we learn why in neural networks sigmoid function is significant and its uses.



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