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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.

Comparison of Activation Functions: Sigmoid, ReLU, Softmax

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?