## Explain Activation Function in Neural Network and its types.

**Ans:** It is a Function used in Neural Network in order to calculate weighted sum and add bias with it. It also introduces non-linearity into output of a Neuron. Back Propagation is possible in Neural network by using Activation Function since the gradients are provided along with error to update the weights and biases. Due to no-linear transformation of input caused by Activation Function makes the Neural Network capable of Learning and Performing Complex tasks.

**Different types of Activation Functions:**

- Binary Step
- Linear Activation
- Sigmoid/ Logistic
- Tanh / Hyperbolic Tangent
- ReLU (Rectified Linear Unit)
- Leaky ReLU
- Parametric ReLU
- Softmax
- Swish

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