What is Neural Network: Overview, Applications, and Advantages

Do you wonder how Google Assistant or Apple’s Siri follow your instructions? Do you see advertisements for products you earlier searched for on e-commerce websites? If you have wondered how this all comes together, it is because of Artificial Intelligence (AI), which works on the backend to offer you rich customer experience. And it is Artificial Neural Networks (ANN) that form the key to train machines to respond to instructions the way humans do.  

A Brief History of AI

The human brain is the most complex organ in the human body. It helps us think, understand, and make decisions. The secret behind its power is a neuron.

Ever since the 1950s, scientists have been trying to mimic the functioning of a neuron and use it to make smarter and better robots. After a lot of trial and error, humans finally created a computer that could recognize human speech. It was only after the year 2000 that people were able to master deep learning (a subset of AI) that was able to see and distinguish between various images and videos.

Before taking a detailed look at what is a neural network, you should be aware of deep learning.

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What is Deep Learning?

Deep learning is a subset of machine learning that asks computers to do what comes naturally to humans: learn by example.

The machine gets trained with images as examples. This process is very different from hardwiring a computer program so that it recognizes something and learns. You don't control how it learns; you control the aspects that go into it. Based on the images that are fed earlier, the computer identifies the object.

Scientists managed to build an artificial form of a neuron(biological) that powers any deep learning-based machine.

After briefly discussing deep learning, let us move on to ‘What is a neural network?’

What is a Neural Network?

To understand how an artificial neuron works, we should know how the biological neuron works. 


These receive information or signals from other neurons that get connected to it.

Cell Body

Information processing happens in a cell body. These take in all the information coming from the different dendrites and process that information.


It sends the output signal to another neuron for the flow of information. Here, each of the flanges connects to the dendrite or the hairs on the next one. 

The image shown below depicts an ANN. 

The network starts with an input layer that receives input in the form of data.

The lines connected to the hidden layers are called weights, and they add up on the hidden layers. Each dot in the hidden layer processes the inputs, and it puts an output into the next hidden layer and lastly, into the output layer. 

Looking at the above two images, you can observe how an ANN replicates a biological neuron.

Input to a neuron - input layer

Neuron - hidden layer

Output to the next neuron - output layer

A neural network is a system of hardware or software patterned after the operation of neurons in the human brain. Neural networks, also called artificial neural networks, are ways of achieving deep learning.

Let us discuss how ANN works in the following section of What is a Neural Network article.

How Do Neural Network Works?

Ever asked Siri a question? The device answers accurately. Let us understand how this virtual assistant accomplishes speech recognition.

Consider a neural network shown below:

There are input, hidden, and output layers on the network. The sentence that the network needs to recognize is: What is the time?

Here, each word comes in as a pattern of sound. The sentence gets sampled into discrete sound waves. 

Let's consider the first word: What 

You can see the waveform is split based on every letter. Now we will split the sound wave for the letter W into smaller segments.

When we analyze the letter ‘W,’ the amplitude varies in the sound wave, as shown below.

We collect the values at intervals and form an array. Different amplitudes come in for different letters, and we feed the array of amplitudes to the input layer. 

Random weights get assigned to each interconnection between the input and hidden layers.

We always start with the random key, as assigning a preset value to the weights takes a significant amount of time when training the model. 

The weights get multiplied with the inputs, and a bias is added to form the transfer function. 

Weights get assigned to the interconnection between the hidden layers. The output of the transfer function is fed as an input to the activation function. The output from one hidden layer becomes the input to the next hidden layer.

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The acoustic model contains the statistical representation of each distinct sound that makes a word. We start building these acoustical models, and as these layers separate them, they'll start learning what the different models represent for different letters.

The lexicon contains the data for different pronunciations of every word. The lexicon is at the end, where we end up with the ABCD, and it identifies the different letters in there.

Finally, we get our output letter. Following the same process for every word and letter, the neural network recognizes the sentence you said or the question you asked.

Note: The terms “acoustic model” and “lexicon” are specific to the domain of understanding speech. When dealing with other input formats, you'll have different labels, but the process will remain the same.

After this detailed working of ANN, let us continue this What is a Neural Network article by looking at the advantages of ANN.

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Advantages of Neural Network

ANN’s outputs aren't limited entirely by inputs and results given to them initially by an expert system. This ability comes in handy for robotics and pattern recognition systems.

This network has the potential for high fault tolerance and is capable of debugging or diagnosing a network on its own. ANN can go through thousands of log files from a company and sort them out. It is presently a tedious task done by administrators.

Nonlinear systems can find shortcuts to reach computationally expensive solutions. We see this in banking where they have an Excel spreadsheet, and then they start building codes around that sheet. In over 20 years, they might create a repertoire of all these functions, and the neural network comes up with the same answers done in days, weeks, or even a month for a large bank.

Let us look at the applications of ANN in the next section of the ‘What is a Neural Network?’ article.

Applications of Neural Network

With an enormous number of applications implementations every day, now is the most appropriate time to know about the applications of neural networks, machine learning, and artificial intelligence. Some of them are discussed below:

Handwriting Recognition

Neural networks are used to convert handwritten characters into digital characters that a machine can recognize.

Stock-exchange prediction

The stock exchange is difficult to track and difficult to understand. Many factors affect the stock market. A neural network can examine a lot of factors and predict the prices daily, which would help stockbrokers. 

Right now, it's still in an initial phase. You should know that there are over three terabytes of data a day just from the US stock exchange. That's a lot of data to dig through, and you have to sort it out before you start focusing on even one stock.

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Traveling issues of sales professionals

This type refers to finding an optimal path to travel between cities in a particular area. Neural networks help solve the problem of providing higher revenue at minimal costs. Logistical considerations are enormous, and here we have to find optimal travel paths for sales professionals moving from town to town. 

Image compression

The idea behind the data compression neural network is to store, encrypt, and recreate the actual image again. We can optimize the size of our data using image compression neural networks. It is the ideal application to save memory and optimize it.

The last section of ‘What is a neural network?’ article lets you understand the future of neural networks.

Future of Neural Networks

With the way AI and machine learning is being adopted by companies today, we could see more advancements in the applications of neural networks. There will be personalized choices for users all over the world. All mobile and web applications try to give you an enhanced customized experience based on your search history.  

Hyper-intelligent virtual assistants will make life easier. If you have ever used Google assistant, Siri, or any of those assistants, you can comprehend how they're slowly evolving. They may even predict your email response in the future. 

We can expect a few intriguing discoveries on algorithms to support learning methods. We are just in the infant stage of applying artificial intelligence and neural networks to the real world. 

Neural networks will be a lot faster in the future, and neural network tools can get embedded in every design surface. We already have a little mini neural network that plugs into an inexpensive processing board, or even into your laptop. Focusing on the hardware, instead of the software, would make devices even faster.

Neural networks will find its usage in the field of medicine, agriculture, physics, discoveries, and everything else you can imagine. Neural networks are also used in shared data systems.

Final Words

The world is wide open for anybody jumping out there to start knowing what is a neural network and exploring the field. The more you understand the concepts, the better you can apply them in various fields. While the adoption of AI is growing with each passing day, companies across the world are facing the AI skills crisis. There is no better time than now to upskill yourself and become an Artificial Intelligence Engineer. You can consider taking Simplilearn’s Artificial Intelligence Engineer Master's Program, which will help you understand neural networks from scratch. Get certified today and build your career in this challenging domain.

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