Industry Use case of Neural Networks !

Gagan Kumar Mahto
7 min readSep 3, 2021


A branch of machine learning, neural networks (NN), also known as artificial neural networks (ANN), are computational models — essentially algorithms. Neural networks have a unique ability to extract meaning from imprecise or complex data to find patterns and detect trends that are too convoluted for the human brain or for other computer techniques. Neural networks have provided us with greater convenience in numerous ways, including through ridesharing apps, Gmail smart sorting, and suggestions on Amazon.

The most groundbreaking aspect of neural networks is that once trained, they learn on their own. In this way, they emulate human brains, which are made up of neurons, the fundamental building block of both human and neural network information transmission.

Neural networks are fundamental to deep learning, a robust set of NN techniques that lends itself to solving abstract problems, such as bioinformatics, drug design, social network filtering, and natural language translation. Deep learning is where we will solve the most complicated issues in science and engineering, including advanced robotics. As neural networks become smarter and faster, we make advances on a daily basis.

How do Neural Networks work?

A neural network is a bundle of neurons connected by synapses. Talking about the artificial one, the role of neurons are played by the units that perform calculations. Each of these “neurons”:

  • receives data from the input layer;
  • processes it performing simple calculations with it;
  • and then transmits it to another “neuron”.

Usually, neural networks consist of three types of neurons:

  • input;
  • output;
  • hidden.

Only single layer neural networks make an exception. They don’t have hidden neurons.

The synapses are responsible for connecting neurons with each other. Each neuron has got multiple outcoming synapses that attenuate or amplify the signal. This makes it possible for the neurons to work in the same way, but to show the different results depending on a certain situation.

Also, neurons are capable of changing their characteristics over a period of time.

So, a typical neural network works like this:

  • it receives certain data through the input layer of neurons;
  • the data is processed by the neurons and passed to the next layer with the help of synapses each of which has its own coefficient;
  • the next layer of neurons receive the information that is the sum of all of all data for neural networks, which are multiplied by the weight coefficients (each by its own);
  • the resulting value is substituted into the activation function, resulting in the formation of output information;
  • information is passed on until it reaches the final exit.

Types of neural networks

There are different kinds of deep neural networks — and each has advantages and disadvantages, depending upon the use. Examples include:

Convolutional neural networks (CNNs) contain five types of layers: input, convolution, pooling, fully connected and output. Each layer has a specific purpose, like summarizing, connecting or activating. Convolutional neural networks have popularized image classification and object detection. However, CNNs have also been applied to other areas, such as natural language processing and forecasting.

Recurrent neural networks (RNNs) use sequential information such as time-stamped data from a sensor device or a spoken sentence, composed of a sequence of terms. Unlike traditional neural networks, all inputs to a recurrent neural network are not independent of each other, and the output for each element depends on the computations of its preceding elements. RNNs are used in fore­casting and time series applications, sentiment analysis and other text applications.

Feedforward neural networks, in which each perceptron in one layer is connected to every perceptron from the next layer. Information is fed forward from one layer to the next in the forward direction only. There are no feedback loops.

Autoencoder neural networks are used to create abstractions called encoders, created from a given set of inputs. Although similar to more traditional neural networks, autoencoders seek to model the inputs themselves, and therefore the method is considered unsupervised. The premise of autoencoders is to desensitize the irrelevant and sensitize the relevant. As layers are added, further abstractions are formulated at higher layers (layers closest to the point at which a decoder layer is introduced). These abstractions can then be used by linear or nonlinear classifiers.

What Tasks do Neural Networks Perform?

Neural networks are highly valuable because they can carry out tasks to make sense of data while retaining all their other attributes. Here are the critical tasks that neural networks perform:

Classification: NNs organize patterns or datasets into predefined classes.

Prediction: They produce the expected output from given input.

Clustering: They identify a unique feature of the data and classify it without any knowledge of prior data.

Associating: You can train neural networks to “remember” patterns. When you show an unfamiliar version of a pattern, the network associates it with the most comparable version in its memory and reverts to the latter.

Our first goal for these neural networks, or models, is to achieve human-level accuracy. Until you get to that level, you always know you can do better.

-Ivan Gomez Data Scientist and Consultant Zencos

Neural networks are widely used in different industries. Both big companies and startups use this technology. Most often, neural networks can be found in all kinds of industries: from eCommerce to vehicle building.


This technology is used in this industry for various purposes. But the most frequent example of artificial neural network application in eCommerce is personalizing the purchaser’s experience. For instance, Amazon, AliExpress, and other eCommerce platforms use AI to show the related and recommended products. The compilation is formed on the basis of the users’ behavior. The system analyzes the characteristics of certain items and shows similar ones. In other cases, it defines and remembers the person’s preferences and shows the items meeting them.

As for more complicated applications of neural networks in eCommerce, there is a very interesting startup called PixelDTGAN. This product is developed to help sellers save the budget on photographers’ services. There is no need to organize photo sets as the special algorithm automatically makes the pictures of the clothes worn by models. All is needed to do is to resize the images of the items to 64*64, and get the result.


In this industry, there are neural network applications for fraud detection, management, and forecasting. A great example of neural network finance applications is SAS Real Time Decision Manager. It helps banks to find solutions for business issues (for instance, whether to give credit to a certain person) analyzing risks and probable profits.

As for financial forecasting, there are plenty of solutions that predict the exchange rate changes. For example, the startup Finprophet is the software that uses a neural network of deep learning for giving the forecast about a wide range of financial instruments like currencies, cryptocurrencies, stocks, futures.


It is very difficult to create and train a neural network for usage in this industry because it requires high accuracy. For many years it seemed to be a fantasy to use this technology for examining patients and diagnosing them. But finally, it has become possible. IBM Watson is the most powerful artificial intelligence in the world. It took 2 years to train the neural network for medical practice. Millions of pages of medical academic journals, medical records, and other documents were uploaded to the system for its learning. And now it can prompt the diagnosis and propose the best treatment pattern based on the patient’s complaints and anamnesis.


Neural networks are widely used for protection from computer viruses, fraud, etc. One of the examples is ICSP Neural from Symantec. It protects from cyber attacks by determining the bad USB devices containing viruses and exploiting zero-day vulnerabilities.

One more sample of using AI and ML for security purposes is Shape security which provides several finance solutions.


This industry needs a lot of management that is to be done manually by employees of many companies. But nowadays, neural networks are capable of routing and dispatching.

For example, Wise Systems is an autonomous system which lets a user:

  • plan routes and monitor them;
  • customize shipping routes in real-time with the help of predictive features.

One more solution is FourKites. This is a visibility program that works in a real-time mode. It helps to plan and monitor routes and predict the time of delivery.

Vehicle building

AI and ML are used in this industry to automate processes. For example, Tesla uses a neural network for the autopilot system in the vehicles. With the help of trained artificial intelligence, it recognizes the road markings, detects obstacles, and makes the road safer for the driver.

The computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful. They are also very well suited for real time systems because of their fast response and computational times which are due to their parallel architecture.

Neural networks also contribute to other areas of research such as neurology and psychology. They are regularly used to model parts of living organisms and to investigate the internal mechanisms of the brain.

Perhaps the most exciting aspect of neural networks is the possibility that some day ‘consious’ networks might be produced. There is a number of scientists arguing that conciousness is a ‘mechanical’ property and that ‘consious’ neural networks are a realistic possibility. What are your thoughts on this?



Gagan Kumar Mahto