What is Activation function in neural networks??
Activation function are the mathematical equations that determine the output of the neural network. This equations are attached to all the neurons in the neural network and determines whether this particular neuron must be activated or not.
When we compare this Activation function with the brain it acts as the receptor to send important information to the neuron.
The main function of the Activation function in the neural network is to extract all the important information for the given weights and features.
There are various types of Activation Functions some of the important Activation functions are mentioned below:
1.RELU ACTIVATION FUNCTION: The main advantage of RELU ACTIVATION FUNCTION is that it does not activate all the neurons at the same time. Due to it all weights and biases are not updated .This makes neurons dead which never gets activated. As only some of the neurons are activated which contains important information it also reduces the problem of overfitting.
2.SIGMOID ACTIVATION FUNCTION: It is also called as logistic activation function. It is used for getting probability of the classes. So the probabilities vary between 0 to 1.It is mainly used for binary classification problems.
3.SOFTMAX ACTIVATION FUNCTION: It is similar to the sigmoid activation function it is also used for classification problems. It mainly classifies multiclass problems.
4.Tanh Activation function: It is also called as Hyperbolic activation function. It is similar to the sigmoid activation function. The advantage of tanh Activation function is that for a negative input that the negative inputs will be mapped strongly negative and the zero inputs will be mapped nearly zero in the tanh graph.
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