NN: Neural Networking
One step ahead into artificial intelligence…
The trends in the technical industry are never at a halt. As the clock needle, it brings a handful of new inventions with very turn. The ongoing trend of reducing manpower not only physically but mentally has brought the concept of Learning to the market.
Learning simply means feeding your machine with some data , creating a model to predict the desired output.
Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions. Deep learning AI is able to learn without human supervision, drawing from data that is both unstructured and unlabeled.
Deep learning applications are used in industries from automated driving to medical devices. Automated Driving: Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. In addition, deep learning is used to detect pedestrians, which helps decrease accidents.
The concept of Neural Network is henceforth a part of this learning process. Deep Learning and machine learning have lured the industries with their facilities to improve, learn, predict, profit their business over the globe.
Let’s Talk About Stats
Driven by the growing interest in artificial intelligence (AI), the global artificial neural network market is projected to grow from $117 million in 2019 to $296 million by 2024, for a compound annual growth rate (CAGR) of 20.5%, according to a study Artificial Neural Network Market by MarketsandMarkets.
The study said the major factors fueling the market growth include the increasing demand to train large volume of data sets with low supervision to drive the market, and the growing need for enhanced processing power, learning ability, and speed of neural networks to drive the growth of the market.
What is Neural Network ??
A neural network 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 in nature.
A training algorithm is a method you use to execute the neural network’s learning process. The Neural Network is constructed from 3 types of layers: Input layer — initial data for the neural network. Hidden layers — an intermediate layer between input and output layer and place where all the computation is done. Output layer — produce the result for given inputs. Each layer consists of neurons that can be increased -decreased by choice.
A Working Example
For instance, you are watching animals let’s say on the street. You came across a bird, a cat, and a dog. The three animals got stored in your mind i.e into the neurons. The next time if you came across the same animal, the neurons predict the output based on the earlier observation you have fed it with. This is how the network of neurons work.
The simple model of ANN is given below. The X is the neuron where is data input ( cat, dog, bird), W is some amount of weight associated with these neurons and transfer function applies the model gives the output.
Neural networks have paved their path not only in the industries but also in the financial market.
Financial Applications Of Neural Network
Back in the day, applications related to the financial domain were handled by Expert Systems, a domain of AI. These have been used in medical diagnosis, fraud detection, prospecting and mineral detection, etc. A major limitation of an Expert system is that they require full information to be given as input and hence deal poorly with uncertainty.
Artificial Neural Networks have been gaining popularity in recent times in dealing with financial applications is they are better at handling uncertainty compared to expert systems. Financial applications primarily involve predicting future events based on past data. Considering the scenarios involving predictions, the following are the primary areas where neural networks can be effectively used:
- Stock Market Prediction/Stock Market Index Prediction Application Evaluation & Underwriting
- Credit Card Customers Search
Stock Market Prediction/Stock Market Index Prediction
Predictions for stock market indices and stock values are handled by the neural networks using the historic data and predicting based on different parameters. The prediction accuracy is enhanced by the choice of variables and the information used for training. Using more hidden layers and more training variables improves the prediction accuracy. For daily NASDAQ stock exchange rate prediction, it was found that a network with three hidden layers and 20–40–20 neurons in hidden layers was the optimized network with an accuracy of 94.08% for the validation dataset. The feed-forward networks are the most widely used architecture because they offer good generalization abilities and are easy to implement.
Loan Application Evaluation
Banks provide loans to the users based on different factors. Neural Networks are employed to underwrite a loan and decide whether to approve or reject the loan application. Banks want to minimize the failure rate of loan applications and maximize the returns on the loans issued.
To give an idea about how this works, let us consider the following example factors that we assume are used in selecting loan applications:
MaritalStatus, Gender, YearlyIncome , TotalChildren, NumberChildrenAtHome, EnglishEducation, HouseOwnerFlag, NumberCarsOwned, CommuteDistance, Region, Age
Target Variable: LoanApproved ( Yes or No)
The training data with the input factors are fed into the neural network so that they get trained. Once they are trained, LoanApproved is found out for any different set of input factors not present in the training set. This is called a test set or real-time data. Based on how the neural network learned the input data, the accuracy of the prediction of the Target Variable is done.
The prediction accuracy depends on the differing input factors as well as the number of hidden layers in the neural networks. Adding a few more hidden layers till the optimum level usually improves the accuracy.
Leading banks and financial services companies are deploying AI technology, including machine learning (ML), to streamline their processes, optimize portfolios, decrease risk and underwrite loans amongst other things.
Similarly, the roots of neural networks have spread throughout the services around the globe