Use Cases Of 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.
Neural Network is a kind of algorithm with approach of predicting data and increasing the efficiency of predations by its own by learning.
What’s the need of Neural Network and Deep Learning.
Prediction task is done by Machine learning also and it is done pretty good but one drawback of Machine learning is that it is not able to do much accurate predictions on big data or in other words we can say that Machine Learning Models are not that effective on Big Data.
In this world of Big data we need to have a way to solve this problem and it is done by Neural Network which comes under Deep Learning which is effective in Big Data as well.
As we feed more and more data to out Learning model its efficiency decreases after a certain point of time whereas in Neural Network the efficiency increases as we feed more and more data to the Neural Network.
Some Advantages of Deep Learning over Machine learning are –
1) Store information on the entire network
Just like it happens in traditional programming where information is stored on the network and not on a database. If a few pieces of information disappear from one place, it does not stop the whole network from functioning.
2) The ability to work with insufficient knowledge:
After the training of ANN, the output produced by the data can be incomplete or insufficient. The importance of that missing information determines the lack of performance.
3) Good falt tolerance:
The output generation is not affected by the corruption of one or more than one cell of artificial neural network. This makes the networks better at tolerating faults.
4) Distributed memory:
For an artificial neural network to become able to learn, it is necessary to outline the examples and to teach it according to the output that is desired by showing those examples to the network. The progress of the network is directly proportional to the instances that are selected.
5) Gradual Corruption:
Indeed a network experiences relative degradation and slows over time. But it does not immediately corrode the network.
6) Ability to train machine:
ANN learn from events and make decisions through commenting on similar events.
7) The ability of parallel processing:
These networks have numerical strength which makes them capable of performing more than one function at a time.
Use cases Of Neural network
Self Driving Cars
Is there anything that screams “future” more than a self-driving car? We’ve spent the last 30 years dreaming of cyberpunk dystopian worlds where androids who dream of electric sheep run from captors by jumping on driverless vehicles. Okay, maybe those vehicles were also able to fly, but you get the point.
Autonomous vehicles aren’t just a dream anymore. Albeit most of them are still just prototypes, they’re definitely a reality nowadays. Dozens of different companies have already invested a substantial amount of money to fuel this technology. And now, self-driven vehicles seem even more indispensable in a world where the coronavirus strengthened the use case for robot drivers and contactless societies. Even a simple algorithm-driven steel bucket on wheels can make the difference if a new pandemic forces the world into a new lockdown.
How else could those vehicles learn how to drive if not through machine learning? Deep learning algorithms are employed by software developers to power computer vision, understand all the details about their surrounding environment, and make smart, human-like decisions. For years, human-driven cars have been equipped with an array of cameras and sensors that record everything from driving patterns to road obstacles, traffic lights, and road signs.
Artificial Neural Networks in Financial Services
When it comes to AI banking and finance, Artificial Neural Networks are well suited to forecasting.
This suitability largely comes from their ability to quickly and accurately analyze large amounts of data.
Artificial Neural Networks are capable of processing and interpreting both structured and unstructured data.
After processing this information Artificial Neural Networks are also able to make accurate predictions.
The more information we can give the system, the more accurate the prediction will be.
Improving the way Banks Operate
The forecasting ability of Artificial Neural Networks is not just confined to the stock market and exchange rate situations.
This ability also has applications in other areas of the financial sector.
Mortgages, overdrafts and bank loans are all calculated after analyzing an individual account holders statistical information.
Traditionally the software that analyzed this information was driven by statistics.
Increasingly banks and financial providers are switching to software powered by Artificial Neural Networks.
This allows for a wider analysis of the applicant and their behavior to be made.
Consequently, this means that the information presented to the bank or financial provider is more accurate and useful.
This allows the bank to make a better-informed decision that is more appropriate to both themselves and the applicant.
Forbes revealed that many mortgage lenders expect this application of systems powered by Artificial Neural Networks will boom in the next few years.
