In this article on Understanding the Role of Activation Functions in Artificial Neural Networks (ANN), I will describe several activation functions.

**Role of Activation Functions in Artificial Neural Networks**

Activation functions are crucial in artificial neural networks (ANNs). Because networks get non-linearity due to activation functions. In fact, without activation functions, ANNs would be nothing more than linear regression models, which are limited in their ability to capture complex, non-linear relationships in data.

As a matter of fact, we apply activation functions to the output of each neuron in the network. Therefore, it transforms the neuron’s input into an output signal that is passed on to the next layer of neurons. Basically, there are many different types of activation functions that we can be use in ANNs, each with its own advantages and disadvantages.

**Examples of Activation Functions in Artificial Neural Networks**

The following list shows some of the most common activation functions.

**Sigmoid**. The sigmoid function is a smooth, S-shaped curve that maps any input value to a value between 0 and 1. So, it is useful in binary classification problems, where we need to interpret the output of the neural network as a probability.**ReLU (Rectified Linear Unit)**. Another important activation function is ReLU. ReLU is a simple, yet powerful activation function that maps any input value less than 0 to 0, and any input value greater than 0 to the input value itself. Also, we use it widely in deep learning because it is computationally efficient and has been shown to work well in practice.**Tanh (Hyperbolic Tangent)**. Tanh is similar to the sigmoid function. However, it maps any input value to a value between -1 and 1. So, it is useful in multi-class classification problems, where the output of the neural network needs to be interpreted as a probability across multiple classes.**Softmax**. Likewise, Softmax is another popular activation function. Also, we use it in multi-class classification problems. Because it maps the output of the neural network to a probability distribution across multiple classes. Therefore, it ensures that the sum of the probabilities is always 1.

In summary, activation functions introduce non-linearity to the neural network. In fact, it is crucial for capturing complex, non-linear relationships in data. Accordingly, there are many different types of activation functions to choose from. Further, each activation function has its own advantages and disadvantages. So, choice of activation function will depend on the specific problem being addressed.

**Further Reading**

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A Brief Introduction of Pandas Library in Python

A Brief Tutorial on NumPy in Python

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