Activation Functions
For optimization purposes, all activation functions must be limited, uninterrupted, monotonic, and continuously distinguishable with respect to the weights. The sigmoid function is the most commonly used activation function. The curve-divagation function and the hyperbolic-tangent function are two further activations that can be obtained. By computing weighted sum and applying bias to it, the activation function determines whether a neuron should be activated or not.
What Is The Importance Of The Sigmoid Function In Neural Networks?
When a neural network uses a linear activation function, the model can only learn linearly separable problems. The neural network can readily learn a non-linearly separable problem with just one hidden layer and a sigmoid activation function in the hidden layer. The sigmoid function can be utilised in neural networks to learn complex decision functions since it provides nonlinear boundaries when using a nonlinear function.
In a neural network, the only non-linear function that can be utilised as an activation function is one that is monotonically rising. The function must also be differentiable across the full real number space.
The weights of a neural network are often learned via gradient descent in a back propagation process. The activation function's derivative is necessary to derive this algorithm. When utilising the back propagation algorithm, the fact that the sigmoid function is monotonic, continuous, and differentiable everywhere, combined with the fact that its derivative can be defined in terms of itself, makes it simple to derive the update equations for learning the weights in a neural network.
Uses
Generally used in the affair subcaste of a double bracket, where the result is either 0 or 1. Because the sigmoid function's value is only between 0 and 1, the outcome can be fluently predicted to be 1 if the value is less than 0.5 and 0 otherwise.
Conclusion
So, here we saw why in neural networks sigmoid function is significant and its uses.
Visit Sigmoid in python to learn more about sigmoid function.
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