Neural network

What is a neural network?

Typically, a neural network is a series or circuit of neurons in the world of computer software and artificial intelligence. It has a set of algorithms that identify the underlying relationships in a set of vast amounts of data using a process that mimics the function of the human brain. In other words: Neural networks involve systems of neurons in organic or artificial environments and help solve problems related to artificial intelligence.  
 
Furthermore, these systems can work with changed input data. Therefore, the network provides the perfect possible results without having to redesign the procedures for the output.  
Today, the concept of neural networks is becoming more common in the development of trading systems. For example, it is applicable to various financial services from estimation and marketing research to fraud detection and risk assessment. As far as stock market price prediction is concerned, the accuracy of neural network prediction varies.

Basic information

As mentioned earlier, neural networks function in the same way as the nervous system of the human brain. In general, a neuron in a neural network is a mathematical function that is used to obtain and classify information with its own architecture or type.  
 
There are layers of interconnected nodes in a neural network. Each node is a perceptron and corresponds to multiple linear regressions. Thus, the perceptron provides the activation function with multiple linear regressions that can be non-linear.  
 
Furthermore, perceptrons are arranged in interconnected layers in a multi-layer perceptron. Therefore, the input layer collects input patterns. Furthermore, the output layer provides output signals or classifications that can map the input patterns.  
 
The input weighting is refined with the hidden layers until the margin of error of the neural networks is minimal. Hypothetically, hidden layers lead to salient features of the output data that have analytical power with respect to the outputs. Therefore, feature extraction is described that fulfils a role similar to statistical procedures such as principal component analysis.

Application

Neural networks are used worldwide for a wide range of applications. For example, they help optimise financial transactions, business planning, business analysis, trading and product maintenance.  
 
They also provide great support for important processes such as time series forecasting, security classification, algorithmic trading, credit risk modelling, proprietary indicator formulation and price splits.  
Neural networks are widely used in business applications such as fraud detection, risk assessment, forecasting and market research solutions. 

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