**Today**, I am going to talk about a very important concept in machine learning; the activation functions in deep learning. This function is responsible for choosing the right neurons to activate to solve a problem. In other words, your activation function is the part of your neural network that decides which neurons should be activated when training a model. It is also responsible for tuning the weights of each neuron to make sure that the model is optimized for the task at hand.

It’s important to choose an activation function that best suits your data and the problem at hand. For instance, if you’re trying to teach a computer how to recognize objects, you would want to use an activation function like ReLU because it allows for great flexibility in how data is processed. Different activation functions are often used depending on the type of problem being solved. Some commonly used functions include sigmoid and ReLU.

**Deep Learning (DL)** is a machine learning technique that has revolutionized the field of artificial intelligence. DL uses neural networks to train computers to perform tasks such as speech recognition or image classification. The key idea behind DL is to create a network of neurons called artificial neurons. These neurons are connected via synapses. Each neuron receives input from other neurons through its synapse. The output of a neuron is determined by the weighted sum of inputs received at the synapse. This weighted sum is known as the activation function.

**The activation **function in deep learning plays a vital role in determining the performance of a neural network. They determine whether a neuron should fire or not. There are different types of activation functions. The most common Deep Learning activation functions are covered in this article. Let’s quickly review the idea of neural networks and their functioning before I move into the specifics of activation functions in deep learning.

Table of Contents

## What is Deep Learning (DL)?

**Deep** learning is a type of machine learning that involves training neural networks, specifically deep neural networks (DNN). DNNs are artificial neural networks designed to work in tandem with human intelligence. These networks have been applied to many different fields including computer vision, speech recognition, natural language processing, and robotics.

## What is the Activation Function?

**The** activation function is a mathematical operation performed on each neuron in a neural network. It’s simply a thing function that returns the output of the node. It is also referred to as the Transfer Function. It determines how much influence a given input has on the output of a neuron. In general, we want our neurons to respond only to inputs that are relevant to their task. We don’t want them to respond to irrelevant inputs. It is merely a thing function that you use to obtain a node’s output.

## Types of Activation Functions in deep learning

The activation function is used to identify whether a neural network’s output is yes or no. It converts the resulting values from 0 to 1 or -1 to 1, and so on (depending upon the function). Three categories of activation functions can be identified linear activation functions, binary step functions, and non-linear activation functions. Now that I’ve covered the fundamental ideas, let’s discuss the most common activation functions for neural networks.

**1 Linear Activation Function:**

A linear neural network is a neural network that has no activation function in any of its layers, known as a linear activation function. The identity function or “no activation” is another name for the linear activation function (multiplied x1. 0). Sometimes referred to as a straight-line function, is proportional to the input. It has a straightforward function, **f (x) = ax + c.** This activation’s drawback is that it cannot be limited to a particular range. The function just spits out the value it was given, doing nothing to the weighted sum of the input.

**2 Binary Step Function: **

A threshold value determines whether a neuron should be triggered, and the binary step function is dependent on this value. If the input is larger than a threshold, the neuron is activated; otherwise, it is deactivated, which prevents its output from being passed on to the following hidden layer.

**3 Non-linear Activation Functions: **

The nonlinear activation functions are classified mostly based on their range or curves. Let’s now examine the activation functions for non-linear activation function-based neural networks.

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** ****Sigmoid activation function**

One of the most popular activation functions is the sigmoid function, commonly called the logistic function. The sigmoid activation function is a mathematical term that describes the relationship between a fixed point and a derivative. It’s used in statistics, industrial engineering, economics, marketing, and artificial intelligence. It helps to model how our senses and cognitive abilities change with experience or exposure to new information. The Sigmoid Function curve has an S-shaped appearance, see the ** figure.** It has the mathematical representation shown in the

*figure.*A sigmoid activation function is a continuous function that maps real numbers. The function is differentiable. The Sigmoid Function curve has an S-shaped appearance, see the *figure.*

Therefore, we can determine the sigmoid curve’s slope between any two points. Although the function is monotonic, its derivative is not.

**Tanh activation function**

Tanh is like a better version of a logistic sigmoid. The tanh function’s range is from (-1 to 1). Tanh is sigmoidal as well (S-shaped). The tanh function is mostly used to classify data into two groups, also known as the hyperbolic tangent Activation Function. In feed-forward networks, tanh and logistic sigmoid activation functions are both employed.

Tanh activation function is a continuous nonlinear function that maps real numbers. The function is differentiable, while the derivative of the function is not monotonic.

**ReLU activation function**

Currently, the ReLU is the activation function that is employed the most globally. Since practically all convolutional neural networks and deep learning systems employ it. ReLU is referred to as Rectified Linear Unit.

Both the function and its derivative have a monotonic behavior. Although it appears to be a linear function, the big catch here is that the ReLU function does not activate all neurons simultaneously. It can be expressed mathematically as in the above figure.

**Leaky ReLU ****activation function**

Over the ReLU activation function, leaky ReLU is an improvement. It possesses all ReLU characteristics and never experiences the issue of dying ReLUs. The advantages of Leaky ReLU are the same as those of ReLU, with the bonus that it supports backpropagation even for negative input values.

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**Parametric ReLU ****activation function**

It is an additional ReLU version that seeks to address the issue of the gradient turning to zero for the left half of the axis.

For negative values, “a” is the slope parameter.

When the leaky ReLU function fails to solve the problem of dead neurons and the relevant information is not successfully transmitted to the next layer, the parameterized ReLU function could be utilized.

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**Exponential Linear Units (ELU)**

Another ReLU version that adjusts the slope of the negative portion of the function is called exponential linear unit (ELU). In contrast to the leaky ReLU and parametric ReLU functions, which define the negative values with a straight line, ELU employs a log curve.

**Scaled Exponential Linear Unit (SELU)**

Another form of ReLU introduced by Günter Klambauer et al. in 2017 is the SELU activation function. Only neural networks made up entirely of dense layers are compatible with SELU. For CNN, it might not function. It has the mathematical representation shown in the below ** figure. **Alpha and lambda values for SELU are predefined.

**Softmax Activation function**

The normalized exponential function, also referred to as softargmax, transforms a vector of K real values into a probability distribution of K potential outcomes. Softmax is a type of activation that is often used in classification tasks. It computes the probability distribution over classes.

** ****Note: **The graphical representation figures of activation functions are taken from v7labs.

**Must read: **What is the neural network?

**FAQ**

**Question: **What is the purpose of the activation function?

**Answer:** The activation function’s objective is to add non-linearity to a neuron’s output.

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**Question: **What is linear and non-linear activation function?

**Answer: **A linear NN is a NN that has no activation function in any of its layers. Non-linear NN has action functions such as ReLU, Sigmoid, or Tanh in any of their layers, or even in more than one layer.

**Question: **What is activation value?

**Answer: **Weighted sum of inputs is the activation value.

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**Question: **Which activation functions are used in deep neural networks?

**Answer: **Currently, the ReLU is the activation function that is employed the most globally. Since practically all convolutional neural networks and deep learning systems employ it.

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**Question: **What are activation functions and their types?

**Answer: **Activation functions determine whether a neuron should be activated. Several types of activation functions are elaborated on in the current article.

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**Question: **What is the best activation function?

**Answer: **The rectified linear activation function, is often known as the ReLU activation function.