Types of Activation Function
- Minu k
- Jul 23, 2022
- 2 min read
Activation Function
The most crucial element in a neural network that determines whether or not a neuron will be engaged and moved to the next layer is the activation function. Simply said, it will determine whether or not the neuron's input to the network is significant to the prediction process. For the neurons that can converge the network, it is also known as a threshold or transformation for this reason.
The output can be normalized using activation functions to fall between 0 and 1 or -1 and 1. Due to its differentiable property, it aids in the backpropagation process. The gradient descent curves' local minima are reached during backpropagation, which also updates the loss function and updates the activation function.
Simply explained, activation mechanisms work as sensors that alert your brain's neurons to the presence of either pleasant or unpleasant odours.
Most activation functions are designed to be non-linear. With the aid of non-linear activation functions, neural networks are capable of computing any complicated function.
Different Activation Function Types
Linear and Non-Linear Activation Functions are two subcategories of activation functions.
Linear Activation Because functions are linear, their output is not restricted by any range. They have no impact on how complicated the data is that neural networks are fed.
Activation that is nonlinear Because they enable the model to generalize or adapt to a large variety of variables while still differentiating between the output, functions are the most widely used.
- ReLU( Rectified Linear unit) Activation function
-Leaky ReLU Activation Function
- Sigmoid Activation Function
- Hyperbolic Tangent Activation Function( Tanh)
Conclusion
The fundamental functions known as activation functions modify the input in a non-linear way, helping it to perceive and complete tasks that are more difficult.
In this , we covered activation functions in neural network and types of activation function.
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