What is Neural Network and what are its applications?

In the training of the model, at one certain point, it came to notice that even after increasing the data, the efficiency of the model remains stagnant then neural networks were adopted. It has the ability to train the model data that have nonlinear relationships between variables, and both can handle interactions between variables. A neural network is more of a black box of interconnected notes that delivers results without an explanation of how the results were derived. Thus, it is difficult or impossible to explain how decisions were made based on the output of the network. If a challenge is made to a decision-based power of a neural network, it is very difficult to explain and justify to non-technical people how decisions were made.

What is Neural Network?

Neural networks (also called “multilayered perceptron”) are computing systems with interconnected nodes that work much like neurons in the human brain, which accept inputs, apply weighting coefficients and feed their output to other neurons which continue the process through the network to the eventual output. It is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial. The concept of neural networks has its roots in artificial intelligence. The smallest unit of the neural network is called a perceptron.

How does Neural Network work?

A neural network works similarly to the human brain’s neural network. A neuron of the human brain collects and classifies information according to a specific architecture. It has the function to

  1. receives data from the input layer

Usually, neural networks consist of three types of neurons.

  1. Input

A neural network contains layers of interconnected nodes. Each node is a perceptron and the way it functions is similar to multiple linear regression. The perceptron feeds the signal produced by a multiple linear regression into an activation function that may be nonlinear. In a multi-layered perceptron (MLP), perceptrons are arranged in interconnected layers. The input layer collects input patterns. The output layer has classifications or output signals to which input patterns may map.

Applications of Neural Network

Neural networks are widely used in different industries. They are used in the following industries

  • eCommerce

eCommerce

This technology is widely used in this sector but the most prominent use is in personalizing the purchaser’s experience. The compilation is formed based on the users’ behavior and the search history. The system analyzes the characteristics of certain items and shows similar ones.

Amazon using Neural Network

Collaborative filtering is the most common way to do product recommendations online. It’s collaborative because it predicts a given customer’s preferences based on other customers. The better way was to base product recommendations not on similarities between customers but correlations between products. With user-based collaborative filtering, a visitor to Amazon.com would be matched with other customers who had similar purchase histories, and those purchase histories would suggest recommendations for the visitor. The recommendation is often modeled as a matrix completion problem. The goal of matrix completion is to fill in the grid with the probabilities that any given customer will buy any particular item. They decided to apply deep neural networks to the problem of matrix completion. The typical deep neural network contains thousands or even millions of simple processing nodes, arranged into layers. Data is fed into the nodes of the bottom layer, which process it and pass their results to the next layer, and so on; the output of the top layer represents the result of some computation. Training the network consists of feeding it lots of sample inputs and outputs. During training, the network’s settings are constantly adjusted, until they minimize the average discrepancy between the top layer’s output and the target outputs in the training examples. Matrix completion methods commonly use a type of neural network called an autoencoder. The autoencoder is trained simply to output the same data it takes as input. But in-between the input and output layers is a bottleneck, a layer with relatively few nodes — in this case, only 100, versus tens of thousands of input and output nodes. To their mild surprise, the item-to-item collaborative-filtering algorithm outperformed the autoencoder. But to their much greater surprise, so did the simple bestseller list. The amazon product recommendation is still securing the first position in the charts and making the customer experience delightful.

Finance

In this sector, a few of the applications are fraud detection, management, and forecasting. Few applications can predict exchange rates, rate of stocks, trend analysis of given stock, etc.

Healthcare

It is very difficult to create and train a neural network for usage in this industry because it requires high accuracy. IBM Watson is the most powerful artificial intelligence in the world for examining patients and diagnosing them. It took almost 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.

Security

Neural networks are widely used for protection from computer viruses, fraud, etc. Intrusion Detection and Prevention Systems (IDS/IPS) are used to detect malicious network activities and prevent intruders from accessing the systems and alerts the user. Typically, they are recognized by known signatures and generic attack forms. This is useful against threats like data breaches. Convolutional neural networks and Recurrent Neural Networks (RNNs) can be applied to create smarter ID/IP systems by analyzing the traffic with better accuracy, reducing the number of false alerts, and helping security teams differentiate bad and good network activities.

Logistics

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 that lets a user:

  • plan routes and monitor them

Conclusion

This was a brief about Neural Networks discussing what are they, how they work, and applications along with Industry use-case.

Thank you!!!