# Neural Network Tutorial: Step-By-Step Guide for Beginners

In the field of machine learning, there are many interesting concepts. Here, in this neural networking tutorial, we’ll be discussing one of the fundamental concepts of neural networks. This article will help you in understanding the working of these networks by explaining the theory behind the same.

After finishing this artificial neural network tutorial, you’ll find out:

• What is a neural network?
• How does a neural network work?
• What are the types of neural networks?

## What are Neural Networks?

A neural network is a system designed to act like a human brain. It’s pretty simple but prevalent in our day-to-day lives.

A complex definition would be that a neural network is a computational model that has a network architecture. This architecture is made up of artificial neurons. This structure has specific parameters through which one can modify it for performing certain tasks.

They have extensive approximation properties. This means they can approximate a function to any level of accuracy irrespective of its dimension. Neural Networks find extensive applications in areas where traditional computers don’t fare too well. From Siri to Google Maps, neural networks are present in every place where Artificial Intelligence is used.

They are a vital part of artificial intelligence operations. Neural networks take inspiration from the human brain and so their structure is similar to one as well.

## How a Neural Network Works?

A neural network has many layers. Each layer performs a specific function, and the complex the network is, the more the layers are. That’s why a neural network is also called a multi-layer perceptron.

The purest form of a neural network has three layers:

1. The input layer
2. The hidden layer
3. The output layer

As the names suggest, each of these layers has a specific purpose. These layers are made up of nodes. There can be multiple hidden layers in a neural network according to the requirements. The input layer picks up the input signals and transfers them to the next layer. It gathers the data from the outside world.

The hidden layer performs all the back-end tasks of calculation. A network can even have zero hidden layers. However, a neural network has at least one hidden layer. The output layer transmits the final result of the hidden layer’s calculation.

Like other machine learning applications, you will have to train a neural network with some training data as well, before you provide it with a particular problem. But before we go more in-depth of how a neural network solves a problem, you should know about the working of perceptron layers first:

### How do Perceptron Layers Work?

A neural network is made up of many perceptron layers; that’s why it has the name ‘multi-layer perceptron.’ These layers are also called hidden layers of dense layers. They are made up of many perceptron neutrons. They are the primary unit that works together to form a perceptron layer. These neurons receive information in the set of inputs. You combine these numerical inputs with a bias and a group of weights, which then produces a single output.

For computation, each neuron considers weights and bias. Then, the combination function uses the weight and the bias to give an output (modified input). It works through the following equation:

combination = bias +weights * inputs

After this, the activation function produces the output with the following equation:

output = activation(combination)

This function determines what kind of role the neural network performs. They form the layers of the network. The following are the prevalent activation functions:

#### The Linear Function

In this function the output is only the combination of the neuron:

activation = combination

The hyperbolic Tangent Function

It is the most popular activation function among neural networks. It is a sigmoid function, and it lies between -1 and +1:

activation = tanh(combination)

#### The Logistic Function

The logistic function is quite similar to the hyperbolic tangent function because it is a kind of sigmoid function, as well. However, it is different because it lies between 0 and 1:

activation = 11 + e-combination

#### The Rectified Linear Unit Function

Just like the hyperbolic tangent function, the rectified linear unit function is also prevalent. Another name for the rectified linear unit function is ReLU. ReLU is equal to the combination when it is equal to or greater than zero, and it’s negative if the combination is lower than (negative) zero.

## So, How Does a Neural Network Work Exactly?

Now that you know what is behind a neural network and how it works, we can focus on the working of a neural network.

Here’s how it works:

1. Information is fed into the input layer which transfers it to the hidden layer
2. The interconnections between the two layers assign weights to each input randomly
3. A bias added to every input after weights are multiplied with them individually
4. The weighted sum is transferred to the activation function
5. The activation function determines which nodes it should fire for feature extraction
6. The model applies an application function to the output layer to deliver the output
7. Weights are adjusted, and the output is back-propagated to minimize error

The model uses a cost function to reduce the error rate. You will have to change the weights with different training models.

1. The model compares the output with the original result
2. It repeats the process to improve accuracy

The model adjusts the weights in every iteration to enhance the accuracy of the output.

## Types of Neural Networks

### 1) Recurrent Neural Network (RNN)

In this network, the output of a layer is saved and transferred back to the input. This way, the nodes of a particular layer remember some information about the past steps. The combination of the input layer is the product of the sum of weights and features. The recurrent neural network process begins in the hidden layers.

Here, each node remembers some of the information of its antecedent step. The model retains some information from each iteration, which it can use later. The system self-learns when its outcome is wrong. It then uses that information to increase the accuracy of its prediction in back-propagation. The most popular application of RNN is in text-to-speech technology.

### 2) Convolutional Neural Network (CNN)

This network consists of one or multiple convolutional layers. The convolutional layer present in this network applies a convolutional function on the input before transferring it to the next layer. Due to this, the network has fewer parameters, but it becomes more profound. CNNs are widely used in natural language processing and image recognition.

### 3) Radial Basis Function Neural Network (RBFNN)

This neural network uses a radial basis function. This function considers the distance of a point from the center. These networks consist of two layers. The hidden layer combines the features with the radial basis function and transfers the output to the next layer.

The next layer performs the same while using the output of the previous layer. The radial basis function neural networks are used in power systems.

### 4) Feedforward Neural Network (FNN)

This is the purest form of an artificial neural network. In this network, data moves in one direction, i.e., from the input layer to the output layer. In this network, the output layer receives the sum of the products of the inputs and their weights. There’s no back-propagation in this neural network. These networks could have many or zero hidden layers. These are easier to maintain and find application in face recognition.

### 5) Modular Neural Network

This network possesses several networks that function independently. They all perform specific tasks, but they do not interact with each other during the computation process.

This way, a modular neural network can perform a highly complex task with much higher efficiency. These networks are more challenging to maintain in comparison to simpler networks (such as FNN), but they also deliver faster results for complex tasks.

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That’s it in our neural network tutorial. You must’ve seen what a variety of tasks these networks can perform. They are used in almost all the technologies we use daily. If you want to find out more about neural networks, you can check our catalogue of courses on artificial intelligence and machine learning.

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