What are 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.
So, How Does a Neural Network Work Exactly?
Here’s how it works:
- Information is fed into the input layer which transfers it to the hidden layer
- The interconnections between the two layers assign weights to each input randomly
- A bias added to every input after weights are multiplied with them individually
- The weighted sum is transferred to the activation function
- The activation function determines which nodes it should fire for feature extraction
- The model applies an application function to the output layer to deliver the output
- Weights are adjusted, and the output is back-propagated to minimize error
The model uses a cost function to reduce the error rate. You will have to change the weights with different training models.
- The model compares the output with the original result
- It repeats the process to improve accuracy
The model adjusts the weights in every iteration to enhance the accuracy of the output.
ADVANTAGES OF NEURAL NETWORKS
As seen already, neural computers have the ability to learn from experience, to improve their performance, and to adapt their behavior to new and changing environments. Unlike conventional rule-based systems, neural networks are not programmed to perform a particular task using rules. Instead, they are trained on historical data, using a learning algorithm.
- Neural networks can provide highly accurate and robust solutions for complex non-linear tasks, such as fraud detection, business lapse/churn analysis, risk analysis, and data mining.
- One of their main benefits is that the method for performing a task need not be known in advance; instead, it is automatically inferred from the data. Once learned, the method can be quickly and easily adjusted to track changes in the business environment.
- A further advantage of neural networks over conventional rule-based systems and fuzzy systems is that once trained, they are far more efficient in their storage requirements and operation; a single mathematical function can replace a large number of rules.
- An added benefit of this more compact mathematical representation is that it introduces a natural form of regularisation or generalization. This makes neural systems extremely robust to noisy, imprecise, or incomplete data.
APPLICATIONS OF NEURAL NETWORKS
Artificial neural networks have become an accepted information analysis technology in a variety of disciplines. This has resulted in a variety of commercial applications (in both products and services) of neural network technology (The applications that neural networks have been put to and the potential possibilities that exist in a variety of civil and military sectors are tremendous.)
Given below are domains of commercial applications of neural network technology.
Business — Marketing, Real Estate
Document & Form Processing — Machine printed character recognition, Graphics recognition, Handprinted character recognition, Cursive handwritten character recognition
Finance Industry — Market trading, Fraud detection, Credit rating
Food industry — Odour/aroma analysis, Product development, Quality assurance
Energy Industry — Electrical load forecasting, Hydroelectric dam operation, Natural gas
Manufacturing Process control — Quality control
Medical & Health Care Industry — Image analysis Drug development, Resource allocation
Science & Engineering — Chemical engineering, Electrical engineering, Weather forecasting
Transportation & Communication
Some applications of neural networks are:
- Forecasting the Behaviour of Complex Systems
- Signal Processing
- Data Compression
- Paint Quality Inspection
- DNA Sequence Analysis
NDUSTRY USE CASES
Top Companies Using Artificial Neural Network(ANN)
- Nvidia Corp.
- Microsoft Corp
Facebook — Chatbot Army
Although Facebook’s Messenger service is still a little…contentious (people have very strong feelings about messaging apps, it seems), it’s one of the most exciting aspects of the world’s largest social media platform. That’s because Messenger has become something of an experimental testing laboratory for chatbots.
Some chatbots are virtually indistinguishable from humans when
conversing via text.
Any developer can create and submit a chatbot for inclusion in Facebook Messenger. This means that companies with a strong emphasis on customer service and retention can leverage chatbots, even if they’re a tiny startup with limited engineering resources.
Of course, that’s not the only application of machine learning that Facebook is interested in. AI applications are being used at Facebook to filter out spam and poor-quality content .
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