Neural Network and its working

What is a 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. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria. The concept of neural networks, which has its roots in artificial intelligence, is swiftly gaining popularity in the development of trading systems.

Basics of Neural Networks

Neural networks, in the world of finance, assist in the development of such process as time-series forecasting, algorithmic trading, securities classification, credit risk modeling and constructing proprietary indicators and price derivatives.

A neural network works similarly to the human brain’s neural network. A “neuron” in a neural network is a mathematical function that collects and classifies information according to a specific architecture. The network bears a strong resemblance to statistical methods such as curve fitting and regression analysis.

A neural network contains layers of interconnected nodes. Each node is a perceptron and is similar to a multiple linear regression. The perceptron feeds the signal produced by a multiple linear regression into an activation function that may be nonlinear.

In a multi-layered perceptron (MLP), perceptrons are arranged in interconnected layers. The input layer collects input patterns. The output layer has classifications or output signals to which input patterns may map. For instance, the patterns may comprise a list of quantities for technical indicators about a security; potential outputs could be “buy,” “hold” or “sell.”

Hidden layers fine-tune the input weightings until the neural network’s margin of error is minimal. It is hypothesized that hidden layers extrapolate salient features in the input data that have predictive power regarding the outputs. This describes feature extraction, which accomplishes a utility similar to statistical techniques such as principal component analysis.

Attributes of Neural Networks

With the human-like ability to problem-solve — and apply that skill to huge datasets — neural networks possess the following powerful attributes:

• Adaptive Learning: Like humans, neural networks model non-linear and complex relationships and build on previous knowledge. For example, software uses adaptive learning to teach math and language arts.
• Self-Organization: The ability to cluster and classify vast amounts of data makes neural networks uniquely suited for organizing the complicated visual problems posed by medical image analysis.
• Real-Time Operation: Neural networks can (sometimes) provide real-time answers, as is the case with self-driving cars and drone navigation.
• Prognosis: NN’s ability to predict based on models has a wide range of applications, including for weather and traffic.
• Fault Tolerance: When significant parts of a network are lost or missing, neural networks can fill in the blanks. This ability is especially useful in space exploration, where the failure of electronic devices is always a possibility.

Advantages of using Neural Networks:

• Neural Networks have the ability to learn by themselves and produce the output that is not limited to the input provided to them.
• The input is stored in its own networks instead of a database, hence the loss of data does not affect its working.
• These networks can learn from examples and apply them when a similar event arises, making them able to work through real-time events.
• Even if a neuron is not responding or a piece of information is missing, the network can detect the fault and still produce the output.
• They can perform multiple tasks in parallel without affecting the system performance.

Neural vs. Conventional Computers

One of the primary differences between conventional, or traditional, computers and neural computers is that conventional machines process data sequentially, while neural networks can do many things at once. Here are some of the other major differences between conventional and neural computers:

• Following Instructions vs. Learning Capability: Conventional computers learn only by performing steps or sequences set by an algorithm, while neural networks continuously adapt their programming and essentially program themselves to find solutions. Conventional computers are limited by their design, while neural networks are designed to surpass their original state.
• Rules vs. Concepts and Imagery: Conventional computers operate through logic functions based on a given set of rules and calculations. In contrast, artificial neural networks can run through logic functions and use abstract concepts, graphics, and photographs. Traditional computers are rules-based, while artificial neural networks perform tasks and then learn from them.
• Complementary, Not Equal: Conventional algorithmic computers and neural networks complement each other. Some tasks are more arithmetically based and don’t require the learning ability of neural networks. Often though, tasks require the capabilities of both systems. In these cases, the conventional computer supervises the neural network for higher speed and efficiency.

As impressive as neural networks are, they’re still works-in-progress, presenting challenges as well as promise for the future of problem-solving.

Neural Network In Business Applications

Neural networks are widely used in different industries. Both big companies and startups use this technology. Most often, neural networks can be found in all kinds of industries: from eCommerce to vehicle building.

So, lets take some examples on the following industries:-

• eCommerce
• Healthcare
• Vehicle Building

eCommerce

This technology is used in this industry for various purposes. But the most frequent example of artificial neural network application in eCommerce is personalizing the purchaser’s experience. For instance, Amazon, AliExpress, and other eCommerce platforms use AI to show the related and recommended products. The compilation is formed on the basis of the users’ behavior. The system analyzes the characteristics of certain items and shows similar ones. In other cases, it defines and remembers the person’s preferences and shows the items meeting them.

Healthcare

It is very difficult to create and train a neural network for usage in this industry because it requires high accuracy. For many years it seemed to be a fantasy to use this technology for examining patients and diagnosing them. But finally, it has become possible.

IBM Watson is the most powerful artificial intelligence in the world. It took 2 years to train the neural network for medical practice. Millions of pages of medical academic journals, medical records, and other documents were uploaded to the system for its learning. And now it can prompt the diagnosis and propose the best treatment pattern based on the patient’s complaints and anamnesis.

Vehicle building

AI and ML are used in this industry to automate processes. For example, Tesla uses a neural network for the autopilot system in the vehicles. With the help of trained artificial intelligence, it recognizes the road markings, detects obstacles, and makes the road safer for the driver.

This is what tesla autopilot sees.

As you can see, AI and ML are the future of all the industries. These technologies help to make decisions, automate the working processes, prevent fraud, and do other important tasks. And they will continue developing.