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In today’s blog post we will take a look of very common activation function, called Rectified Linear Unit (ReLU). But first – what is activation functions in general? In short, it is a critical part of neural networks. Without it, neural networks would be linear models, which are not able to learn complex patterns. The choice of activation function depends on the specific problem that the neural network is trying to solve. There is no one-size-fits-all solution. Example of such functions:

- Sigmoid: This function is S-shaped and has a range of 0 to 1. It is often used in classification problems.
- Tanh: This function is also S-shaped and has a range of -1 to 1. It is often used in regression problems.
- ReLU: This function is linear for positive inputs and 0 for negative inputs. It is a popular choice for neural networks because it is computationally efficient and does not suffer from the vanishing gradient problem. It is the main topic of this article.
- Leaky ReLU: This function is similar to ReLU, but it has a small slope for negative inputs. This helps to prevent the vanishing gradient problem.

## So, what is ReLU?

ReLU is used in artificial neural networks to introduce non-linearity to the model. It is one of the most popular activation functions in deep learning, and it is known for its simplicity and efficiency.

The ReLU activation function is defined as follows:** **

f(x) = max(0, x)

This means that the output of the ReLU function is equal to the input if the input is positive. And it is equal to 0 if the input is negative.

### Python implementation

import numpy as np def relu(x): return(np.maximum(0, x))

### Visual representation

## ReLU advantages

The ReLU activation function has several advantages. First, it is very simple to implement. Second, it is very efficient to compute. Third, it does not suffer from the vanishing gradient problem, which is a problem that can occur with other activation functions.

The vanishing gradient problem occurs when the derivatives of the activation function approach 0 as the inputs approach 0. This can make it difficult for the neural network to learn, because the errors cannot be backpropagated through the network.

The ReLU activation function does not suffer from the vanishing gradient problem because its derivative is always 1 for positive inputs. This means that the errors can always be backpropagated through the network, which makes it easier for the neural network to learn.

## Example usages

Here are some examples of how ReLU activation is used in deep learning:

- Image classification;
- Natural language processing;
- Speech recognition;
- Machine translation.

ReLU activation is a versatile tool that can be used in a variety of deep learning applications. It is a simple and efficient way to introduce non-linearity to neural networks, and it can help to improve the performance of the models.