Artificial Neural Networks Types and Applications | How Neural Networks Work?

Artificial neural networks (ANNs) are computational systems that are mysteriously inspired by the structure and functions of biological neural networks.
An artificial neural network is an information processing model where complex relationships between input and output are found.

Artificial neural networks are computing systems that are inspired by the biological neural networks

Artificial Neural Networks Types and Applications | How Do Neural Networks Work?

Artificial Neural Networks

The term "Neural" is derived from the basic functional unit of the human (animal) nervous system "neurons" or "nerve cells". 

A neural network is a set of algorithms that testify to the inherent relationship in a set of data similar to the human brain. 
A neural network is inspired by the human (animal) brain and designed to form neurons in the brain and perform specific functions.

In information technology (IT), Artificial Neural Networks (ANN) are computing systems that are inspired by the biological (real) neural networks to perform specific tasks. 
Artificial neural networks are a variety of deep learning technologies that fall under the field of artificial intelligence applications.

Commercial applications of artificial neural networks generally focus on solving pattern recognition or complex signal processing problems. 
Examples of important commercial applications of neural networks since 2000 include speech-to-text transcription, facial pattern recognition, and handwriting recognition software for check processing, data analytics in the oil and gas industry and weather forecasting.

Artificial neural networks (ANNs) are computational systems that are mysteriously inspired by the structure and functions of biological neural networks.

An artificial neural network is an information processing model where complex relationships between input and output are found.

History of Neural Networks

Artificial neural networks certainly represent powerful modern computer technology.  
The idea of neural networks begins in 1943s when two researchers from the University of Chicago, Warren McCulloch - a neuroscientist, and Walter Pitts - a mathematician, wrote a paper on how neurons might work.

Neural networks in the 1950s were a fertile area for computer neural network research, including the Perceptron system that achieved visual recognition based on the fly's compound eye.

The first multi-layered neural network was developed in 1975, paving the way for further development of neural networks. It was an achievement that some thought it was impossible less than a decade ago.

In 1982, the point-of-interest in neural networks was renewed dramatically when Princeton University professor John Joseph Hopfield invented an associative neural network. 
The innovation was that the data can be transmitted in a two-way direction and not just unidirectionally as before, this invention is also known as the Hopfield network. 

Nowadays artificial neural networks are gaining widespread popularity and great development.

How Do Neural Networks Work?

The neural network typically includes a large number of processors running in parallel and arranged in levels, ie the first layer that receives the raw input information is similar to the optic nerves in human visual processing, and each successive layer receives the output from the previous layer, then the last layer outputs the system processing products.

Each node has its own small field of knowledge, including what you saw or any databases that were originally programmed or developed for themselves, and the layers are highly interconnected, which means that each node in layer n will be connected to many nodes in Layer n-1.

Neural networks are known to be adaptive, which means that they adjust themselves when they learn from initial training, and later provide more information about the world.

How to Learn Neural Networks?

Unlike other algorithms, neural networks cannot be programmed directly with their deep learning for the task, and instead, resemble a child's developing brain, ie, they need to indoctrinate information.

Learning Techniques in Neural Networks

There are three learning techniques that are commonly used:

Supervised learning: This learning technique is the simplest where there is a data set described and experienced by the computer, and the algorithm is modified so that it can process the dataset to get the desired result.

Unsupervised learning: This learning technique is used in cases where a parameterized data set is not available to learn from and where the neural network analyzes the data set and the cost function then tells the neural network about the long run from the target, and then the neural network adapts to increase the accuracy of the algorithm.

Reinforcement learning: In this learning technique, the neural network is enhanced to obtain positive results and to penalize a negative result, forcing the neural network to learn overtime.

Types of Artificial Neural Networks

Neural networks are sometimes described in-depth, including the number of layers between inputs and outputs, or so-called hidden layers. 
This is why the use of the term neural network is almost synonymous with deep learning, and it can also be described by the number of hidden nodes the model has or the number of inputs and outputs per node.

Single-Layer Perceptron in Neural Networks

This neural network contains two input units and one output unit without any hidden layers.

Multilayer Perceptron Neural Network

This neural network contains more than one hidden layer of neurons.

Feed-Forward Neural Network

The simplest type of neural network is the feedforward neural network – artificial neuron. 
This type of artificial neural network algorithm passes information directly from input to the processing nodes to results, and may not contain hidden node layers, making its work more understandable.

Radial Basis Function Neural Network

Radial basis function neural network is an artificial neural network. This is similar to the feed-forward neural network except it uses radial basis functions as activation functions.

Recurrent Neural Network (RNN)

More complicated neural networks are recurrent neural networks (RNN) and long short term memory (LSTM) where these deep learning algorithms preserve the output of the processing nodes and enter the result in the model, this is how it is said that the model is learning.

Convolutional Neural Network

Convolutional neural networks are popular today especially in the field of image recognition. 
This specific type of neural network algorithm has been used in many of the most advanced applications in artificial intelligence, including facial recognition, text numbering, and natural language processing.

Modular neural network

A modular neural network is the combined structure of various types of artificial neural networks such as the recurrent neural network, Hopfield network, multilayer perceptron, etc., which are integrated as a single module in the network.

Applications of artificial neural networks

Artificial neural networks have become a very common and useful tool for solving many problems such as pattern recognition, classification, dimension reduction, regression, machine translation, anomalies detection, systematic prediction, clustering, and decision-making.

Image recognition was one of the first areas to be successfully applied to neural networks, but the uses of neural networks have expanded to many other areas including:

⇒Natural language processing, translation and language generation.

⇒Cursive handwriting recognition.

⇒Speech recognition network.

⇒Optical character recognition.

⇒Stock market prediction.

⇒Foreign exchange trading systems.

⇒Portfolio selection and management.

⇒Forecasting weather patterns.

⇒Driver performance management and real-time route optimization.

⇒Data analytics in the oil and gas industry.

⇒Drug discovery and development.

⇒Credit card fraud detection.

⇒Detection of bombs in suitcases.

⇒Myocardial Infarction Prediction.

⇒Diagnosis of dementia and Alzheimer's disease.

These are just a few of the specific areas in which neural networks are applied today.