Neural Network & its Use Cases.

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. Neural networks (NN), also known as Artificial Neural networks (ANN).

The architecture of an artificial neural network:

To understand the concept of the architecture of an artificial neural network, we have to understand what a neural network consists of. In order to define a neural network that consists of a large number of artificial neurons, which are termed units arranged in a sequence of layers. Lets us look at various types of layers available in an artificial neural network.

Artificial Neural Network primarily consists of three layers:

Input Layer:

As the name suggests, it accepts inputs in several different formats provided by the programmer.

Hidden Layer:

The hidden layer presents in-between input and output layers. It performs all the calculations to find hidden features and patterns.

Output Layer:

The input goes through a series of transformations using the hidden layer, which finally results in output that is conveyed using this layer.

Why Do We Use Neural Networks?

Neural networks’ human-like attributes and ability to complete tasks in infinite permutations and combinations make them uniquely suited to today’s big data-based applications. Because neural networks also have the unique capacity (known as fuzzy logic) to make sense of ambiguous, contradictory, or incomplete data, they are able to use controlled processes when no exact models are available.

Some Use Cases :

Neural networks in medicine

Neural Networks (ANN) are currently a ‘hot’ research area in medicine and it is believed that they will receive an extensive application to biomedical systems in the next few years. At the moment, the research is mostly on modelling parts of the human body and recognizing diseases from various scans (e.g. cardiograms, CAT scans, ultrasonic scans, etc.).

Neural networks are ideal in recognizing diseases using scans since there is no need to provide a specific algorithm on how to identify the disease. Neural networks learn by example so the details of how to recognize the disease are not needed. What is needed is a set of examples that are representative of all the variations of the disease. The quantity of examples is not as important as the ‘quantity’. The examples need to be selected very carefully if the system is to perform reliably and efficiently.

Neural networks in Security

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.

 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.

 Improving Search Engine Functionality

During 2015 Google I/O keynote address 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.

Within a few months, Google was already noticing improvements in search results.

The company reported that its error rate had dropped from 23% down to just 8%.

Google’s application shows that neural networks can help to improve search engine functionality.

Similar Artificial Neural Networks can be applied to the search feature on many e-commerce websites.

This means that many companies can improve their website search engine functionality.

This allows customers with only a vague idea of what they want to easily find the perfect item.

ThankYou For Reading!