Neural Networks and use cases in Industries

In today`s world One of the key parts of cutting edge AI technology, Artificial Neural Networks (ANNs) are becoming too important and commonplace to ignore.

However, Artificial Neural Networks and the role that they play can be a difficult concept to understand.

In this article, I’ll explain exactly what Artificial Neural Network is and how they work.

What is 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 reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning.

Artificial neural network can be defined as a system of interconnected elements, which takes input and after experience shows features like human behavior. As elements are interconnected , elements receive input from another elements.

An artificial neural network (ANN) is the piece of a computing system designed to simulate the way the human brain analyzes and processes information. It is the foundation of artificial intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards. ANN’s can be called as basic learning devices in the form of software or hardware.

ANN is used in various industries like Robotics, manufacture, Banking & finance, Insurance, IT, Telecommunication, operation Management, business analytics and many more.

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.
• Consequently ANN’s play an increasingly important role in the development of artificial intelligence.
• The rise in importance of Artificial Neural Network’s is due to the development of “backpropagation”.
• This technique allows the system’s hidden layers to become versatile.
• Adapting to situations where the outcome doesn’t match the one originally intended.
• 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.
• This allows the system to become more versatile.
• Different layers are able to analyse and extract different features.
• This process allows the system to identify new data or images.
• It also allows for unsupervised learning and more complex tasks to be undertaken.

 Neural Networks are 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 colors.

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.

Amazon has reported sales increases of 29% following improvements to its recommendation systems.

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. improvements to its recommendation systems.

Conclusion

The computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful. Furthermore there is no need to devise an algorithm in order to perform a specific task; i.e. there is no need to understand the internal mechanisms of that task. 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.

Thank you for reading this article hope you find it informative!!