Neural Network

What are Neural Networks?

Neural networks are a set of algorithms, they are designed to mimic the human brain, that is designed to recognize patterns. They interpret data through a form of machine perception by labeling or clustering raw input data.  Our brain is a very complex structure, made up of a network of neurons.

First, we need to talk about neurons, the basic unit of a neural network. A neuron takes inputs, does some mathematical operations with them, and produces one output. Here’s what a 2-input neuron looks like:

Neural networks help us in making cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on. Neural networks can also extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.

Applications in Deep Learning and Artificial Intelligence

Artificial neural networks are a form of deep learning. They are also one of the main tools used in machine learning. The development of deep learning neural networks has also helped in the development of Artificial Neural Networks. Deep learning neural networks are networks made up of multiple layers. Different layers are able to analyse and extract different features. It also allows for unsupervised learning and more complex tasks to be undertaken.

How do Artificial Neural Networks Work?

As we have seen Artificial Neural Networks are made up of a number of different layers. Each layer houses artificial neurons called units. These artificial neurons allow the layers to process, categorize, and sort information. Alongside the layers are processing nodes. Each node has its own specific piece of knowledge. This knowledge includes the rules that the system was originally programmed with. It also includes any rules the system has learned for itself. This makeup allows the network to learn and react to both structured and unstructured information and data sets. Almost all artificial neural networks are fully connected throughout these layers, each connection is weighted. The heavier the weight, or the higher the number, the greater the influence that the unit has on another unit.

The first layer is the input layer. This takes on the information in various forms. This information then progresses through the hidden layers where it is analysed and processed. By processing data in this way, the network learns more and more about the information. Eventually, the data reaches the end of the network, the output layer. Here the network works out how to respond to the input data. This response is based on the information it has learned throughout the process. Here the processing nodes allow the information to be presented in a useful way.

Artificial Neural Networks Usecases

Developing Targeted Marketing Campaigns

Through unsupervised learning, Artificial Neural Networks are able to identify customers with a similar characteristic, this allows businesses to group together customers with similarities, such as economic status or preferring vinyl records to downloaded music. Supervised learning systems allow Artificial Neural Networks to set out a clear aim for your marketing strategy. Like unsupervised systems, they can also segment customers into similar groupings. However supervised learning systems are also able to match customer groupings to the products they are most likely to buy, this application of technology can increase profits by driving sales.

Improving Search Engine Functionality

During 2015 Google in San Francisco, Google revealed they were working on improving their search engine. These improvements are powered by a 30 layer deep Artificial Neural Network. This depth of layers, Google believes, allows the search engine to process complicated searches such as shapes and colours. Using an Artificial Neural Network allows the system to constantly learn and improve. This allows Google to constantly improve its search engine.

Neural Networks in the Retail Sector

As we have noted, Artificial Neural Networks are versatile systems, capable of dealing reliably with a number of different factors. This ability to handle a number of variables makes Artificial Neural Networks an ideal choice for the retail sector. Artificial Neural Networks are, when given the right information, able to make accurate forecasts. These forecasts are often more accurate than those made in the traditional manner, by analysing statistics. This can allow accurate sales forecasts to be generated.

Fraud Detection Applications

As technology advances, and more importance is placed on online transactions, fraudsters are also becoming more sophisticated. Deep learning and Artificial Neural Networks applications are powering systems capable of detecting all forms of financial fraud. For example, this application can identify unusual activity, such as transactions occurring outside the established time frame.

Facial Recognition Software

Technology companies have long been working toward developing reliable facial recognition software. One company leading the way is Facebook. For a number of years now they have been using the facial recognition technology to auto-tag uploaded photographs.

The Effects on Insurance Provision

Artificial Neural Networks have a number of different applications in the insurance industry. Firstly, as in marketing applications, Artificial Neural Networks allow for segmentation of policyholders, this grouping allows companies to determine and offer appropriate pricing plans. Consequently applying Artificial Neural Networks allows for the correct level of provision to be offered. It also allows for special offers to be made to encourage customers to renew policies.

CONCLUSION

So Finally through this blog We can say that today neural network playing the important role for solving the Real Use cases in Industry.

Thank you !