The ReLU (Rectified Linear Unit) activation function is a popular activation function used in deep learning neural networks. It is defined as:
f(x) = max(0, x)
This function returns the input value x if it is positive, and returns zero otherwise. In other words, it "rectifies" the input, producing a linear output for positive values and zero for negative values.
The ReLU function has several desirable properties, including:
<ol>
<li>Simplicity: The function is simple to compute and does not require complex calculations.
</li>
<li>Sparsity: The function produces sparse representations, where many of the outputs are zero, which can be useful for reducing the amount of computation required in a network.
</li>
<li>Non-linearity: Despite its linear behavior for positive inputs, the ReLU function is non-linear overall, which allows neural networks to learn complex nonlinear relationships between inputs and outputs.
</li>
</ol>
The ReLU function has become a popular choice for activation functions in deep learning due to its simplicity, efficiency, and effectiveness in many applications.

The ReLU (Rectified Linear Unit) activation function is a popular activation function used in deep learning neural networks. It is defined as:

f(x) = max(0, x)

This function returns the input value x if it is positive, and returns zero otherwise. In other words, it "rectifies" the input, producing a linear output for positive values and zero for negative values.

The ReLU function has several desirable properties, including:

1. Simplicity: The function is simple to compute and does not require complex calculations.
 
2. Sparsity: The function produces sparse representations, where many of the outputs are zero, which can be useful for reducing the amount of computation required in a network.
 
3. Non-linearity: Despite its linear behavior for positive inputs, the ReLU function is non-linear overall, which allows neural networks to learn complex nonlinear relationships between inputs and outputs.
 

The ReLU function has become a popular choice for activation functions in deep learning due to its simplicity, efficiency, and effectiveness in many applications.

Relu activation function