Neural Network and its industry use cases

6 min readSep 26, 2021

What are Neural networks?

Neural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data. They are used in a variety of applications in financial services, from forecasting and marketing research to fraud detection and risk assessment.

How does 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:

The input layer
The hidden layer
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 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:

Information is fed into the input layer which transfers it to the hidden layer
The interconnections between the two layers assign weights to each input randomly
A bias added to every input after weights are multiplied with them individually
The weighted sum is transferred to the activation function
The activation function determines which nodes it should fire for feature extraction
The model applies an application function to the output layer to deliver the output
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.

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

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

Use Cases of Neural Networks

Artificial Neural Networks can be used in a number of ways. They can classify information, cluster data, or predict outcomes. ANN’s can be used for a range of tasks. These include analyzing data, transcribing speech into text, powering facial recognition software, or predicting the weather.

Case Study: YELP

Yelp, is a crowd-sourced local business review and social networking site. The site has pages devoted to individual locations, such as restaurants or schools, where Yelp users can submit a review of their products or services using a one to five star rating system.

The company is using artificial intelligence to better serve its millions of users. Yelp is enhancing the consumer the customer services and experiences by letting users post reviews along with images attached to them. Use of picture classification technology by the company has allowed companies to compile, manage, label and categories the images which are a reflection of the data of the reviewers and their reviews.

They say that they use deep learning which helps them most to identify fake and real reviews. In Yelp’s case, its software is using image analysis techniques to identify color, texture, and shape, meaning it can recognize the presence of say, burritos, or whether a restaurant has outdoor seating.

At this point, “the company is now able to predict attributes like ‘good for kids’ and ‘ambiance is classy’ with 83% accuracy” based solely on photos (arguably a more reliable source of information than user-submitted reviews, which can often be terribly biased or just factually incorrect). Soon, Yelp will be able to use this information “to auto-caption images, improve search recommendations, and better select an assortment of images to feature on businesses’ listing pages.”

So AI and neural networks can be applied to a vast number of use cases.

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