Artificial neural networks are based on biological neurons in the human body that activate in response to certain stimuli, causing the body to perform a connected action. Artificial neural nets are made up of multiple layers of interconnected artificial neurons that are powered by activation functions that turn them on and off. There are specific values that neural nets learn in the training process, just like standard machine learning algorithms.

**What is Activation Function?**

As previously stated, activation functions provide the ultimate value provided by a neuron; but, what is an activation function and why do we need it?

So, an activation function is just a simple function that changes its inputs into outputs with a specific range of values. The sigmoid activation function, for example, receives input and translates the resulting values between 0 and 1 in a different way than other types of activation functions.

**Second Half**

One of the reasons for including this function in an artificial neural network is to aid the network in learning complex patterns in input. These functions provide artificial neural networks nonlinear real-world features. In a simple neural network, x represents inputs, w represents weights, and f (x) represents the value sent to the network’s output. The ultimate output or the input of another layer will then be this.

The output signal becomes a simple linear function if the activation function is not used. Without an activation function, a neural network will behave like a linear regression with minimal learning power. However, as we feed our neural network complicated real-world data such as picture, video, text, and sound, we want it to learn non-linear states.

**Now We Focus On ReLU Activation Function:-**

The activation function in a neural network is responsible for converting the node’s summed weighted input into the node’s activation or output for that input.

The rectified linear activation function, or relu activation function for short, is a piecewise linear function that, if the input is positive, outputs the input directly; else, it outputs zero. Because a model that utilises it is quicker to train and generally produces higher performance, it has become the default activation function for many types of neural networks.

Another non-linear activation function that has gained prominence in the deep learning sector is the ReLU Activation Function. Rectified Linear Unit (ReLU) is an abbreviation for Rectified Linear Unit. The key benefit of employing the ReLU function over other activation functions is that it does not simultaneously stimulate all of the neurons.

The neurons will only be silenced if the linear transformation’s output is less than 0. The diagram below can assist you in better comprehending this-

f(x)=max(0,x)

Unlike sigmoid and tanh functions, the ReLU function is computationally efficient since only a small number of neurons are activated when there is a negative input value.. The ReLU python function is as follows:

def relu_function(x):

if x<0:

return 0

else:

return x

relu_function(7), relu_function(-7)

Output:

(7, 0)

Let’s look at the gradient of the ReLU function.

f'(x) = 1, x>=0

= 0, x<0

When you look at the graph from the negative side, you’ll observe that the gradient value is zero. The result is that some neurons do not get their weights or biases updated during the backpropogation process.. The ‘Leaky’ ReLU function takes care of this.

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