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# Comparison of Activation Functions - Sigmoid, ReLU, and Softmax  

Activation functions transform input values in an artificial neural network and transmit them to the next layer.

The `Sigmoid`, `ReLU (Rectified Linear Unit)`, and `Softmax` functions that you have learned so far each have their own characteristics, advantages, and disadvantages.

<br />  

## Comparison of Activation Functions  

| Function    | Output Range    | Features and Advantages                           | Disadvantages and Limitations  |
|-------------|-----------------|------------------------------------------------|--------------------------------|
| Sigmoid     | (0, 1)          | Probabilistic interpretation, suitable for binary classification | Vanishing gradient problem for large values |
| ReLU        | (0, ∞)          | Avoids vanishing gradient problem, computationally efficient | Neuron deactivation for values ≤ 0 |
| Softmax     | (0, 1)          | Suitable for multi-class classification, provides probability values | One class value can influence other classes |

<br />  

Activation functions play a critical role in determining a neural network’s performance.	

It's important to choose the appropriate activation function based on the problem's characteristics.

In the next lesson, we will take a brief quiz to review what we've learned so far.

The softmax function is well-suited for multi-class classification tasks, as it converts raw scores into probabilities for each class. This allows the model to make a clear choice among multiple possible classes.

Function	Output Range	Features and Advantages	Disadvantages and Limitations
Sigmoid	(0, 1)	Probabilistic interpretation, suitable for binary classification	Vanishing gradient problem for large values
ReLU	(0, ∞)	Avoids vanishing gradient problem, computationally efficient	Neuron deactivation for values ≤ 0
Softmax	(0, 1)	Suitable for multi-class classification, provides probability values	One class value can influence other classes

Comparison of Activation Functions - Sigmoid, ReLU, and Softmax

Comparison of Activation Functions

Which of the following activation functions is most suitable for multi-class classification?