Neural Networking

Neural network refers to a range of algorithms that are meant to reproduce the work of the human brain via artificial intelligence. The term received its name as the determined principles of a mathematical model are reminiscent of our nervous system functioning.

Neural networks are able to adapt to alternations of input data. That’s why the system seeks for the optimal results, while converting output is not needed.

Essence of Neural Network

The human brain consists of neurons connected by synapses and transmitting electrochemical impulses. The neural network, on the other hand, is made up of artificial neurons, i.e. the computational elements.

In fact, it is a system that comprises a great number of neurons or processors. These processors are quite simple on a standalone basis, but after being connected into a large system, neurons are able to perform complex tasks of collecting, analyzing, and optimizing data, along with making decisions.

In a trading realm, neural networks facilitate a working out of various processes, for example, time series forecasting, types of securities, credit risk modeling, algorithmic trading, as well as structuring cost derivatives. 

An artificial neural network consists of three components:

• Input layer;
• Hidden (computational) layers;
• Output layer.

Types of Neural Network

There exist dozens of neural networks that differ in architecture, operational features and application areas. 

Meanwhile, the following three types are considered the most common:

1. Recurrent neural networks (RNN). The structure can process a series of events in time or sequence, in order to obtain a calculation result. RNN is used for language modeling and text generation, machine translation, speech recognition and other tasks.
2. Convolutional neural networks (CNN). The systems are used for image classification, object recognition, prediction, natural language processing and other tasks.
3. Deep neural network (DNN). The type anticipates several levels of processing that makes this system more flexible than others.

Neural Network in real life

Actually, neural networks are already widely used in various areas of life. For instance, in face recognition (to land criminals), diagnosing diseases, and performing as voice assistants. Their appliance in the private sector has also risen substantially, in order to assess employee’s performance, approve loans, communicate via chatbots, or manage call centers.

Let’s consider some of the most widespread application areas of neural networks:

• Finance. Assessing the risk of loan default, predicting bankruptcy, estimating the real estate value and ensuring the security of card transactions.
• Marketing. Establishing shopping models, segmenting customers, along with replacing copywriters.
• Politics. Analyzing opinions, predicting ratings dynamics, identifying social factors and determining political views by appearance.
• IT. Simplifying and speeding up the application development, creating chat bots, and customizing search engine algorithms.
• Security. Recognizing face, voice, fingerprints, along with detecting fakes.

Pros and cons of the concept

Neural networks are capable of parallel data processing and self-learning. However, this is only a small fraction of all benefits in regards to the system. 

So the key advantages of neural networks are:

1. Adaptability. Neurons adapt to the alternations of the environment. Anyway, the higher the adaptive capacity of the system is, the more stable its operation will be.
2. Efficiency. The algorithms are similar to human intuition. That’s why the neural network is able to make decisions, for example, on the financial standing of the company.
3. Flexibility. Perceptrons and connections between them can be combined in various ways, leading to unpredictable results.
4. Work with minor losses. Even if a number of neurons are damaged, they give correct and logical answers. Functionality can only be affected by severe setbacks.
5. Sustainability. The algorithms independently determine the input parameters that don’t produce necessary results for analysis and filter them out.

It may seem that neural networks are the ideal solution for the current problems. But despite the wide range of possibilities, there exists a number of shortcomings. 

Most design approaches are based on practical experience and may not be accurate enough. Therefore, there are no unambiguous solutions for emerging issues. Moreover, creating a self-learning system takes a lot of time and sometimes reaches an impasse. Professionals often have problems finding a sufficient number of training examples.