What Is Artificial Neural Networks?

Artificial Intelligence

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.

AI is accomplished by studying how the human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.

Subsets of Artificial Intelligence

Artificial Intelligence is an umbrella term. There are two subsets of Artificial Intelligence: Machine Learning and Deep Learning.

Machine Learning

Machine learning is a branch of artificial intelligence in which a program or machine uses a set of algorithms to find patterns in the dataset(s). Above all, we don’t have to write individual instructions for every action. As machine learning models capture more and more data, they become smarter and self-improving.

Deep Learning

Further development of machine learning has led to a different sub-category, i.e., Deep Learning. Deep Learning makes use of artificial neural networks that consist of layers of networks working on different parameters to give the desired output.

What Is An Artificial Neural Network?

ANN is a non-linear model that is widely used in Machine Learning and has a promising future in the field of Artificial Intelligence.

Artificial Neural Network is analogous to a biological neural network. A biological neural network is a structure of billions of interconnected neurons in a human brain. The human brain comprises neurons that send information to various parts of the body in response to an action performed.

Similar to this, an Artificial Neural Network (ANN) is a computational network in science that resembles the characteristics of a human brain. ANN can model as the original neurons of the human brain, hence ANN processing parts are called Artificial Neurons.

ANN consists of a large number of interconnected neurons that are inspired by the working of a brain. These neurons have the capabilities to learn, generalize the training data and derive results from complicated data.

These networks are used in the areas of classification & prediction, pattern & trend identifications, optimization problems, etc. ANN learns from the training data (input and target output known) without any programming.

The learned neural network is called an expert system with the capability to analyze information and answer the questions of a specific field.

The formal definition of ANN given by Dr.Robert Hecht-Nielson, inventor of one first neuro computers is:

“…a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs”. 

Characteristics Of ANN

• Non Linearity: The mechanism followed in ANN for the generation of the input signal is nonlinear.
• Supervised Learning: The input and output are mapped and the ANN is trained with the training dataset.
• Unsupervised Learning: The target output is not given, so the ANN will learn on its own by discovering the features in the input patterns.
• Adaptive Nature: The connection weights in the nodes of ANN are capable of adjusting themselves to give the desired output.
• Biological Neuron Analogy: The ANN has a human brain-inspired structure and functionality.
• Fault Tolerance: These networks are highly tolerant as the information is distributed in layers and computation occurs in real-time.

Structure Of ANN

Artificial Neural Networks are processing elements either in the form of algorithms or hardware devices modeled after the neuronal structure of a human brain cerebral cortex.

These networks are also simply called Neural Networks. The NN is formed of many layers. The multiple layers that are interconnected are often called “Multilayer Perceptron”. The neurons in one layer are called “Nodes”. These nodes have an “Activation function”.

The ANN has 3 main layers:

• Input Layer: The input patterns are fed to the input layers. There is one input layer.
• Hidden Layers: There can be one or more hidden layers. The processing that takes place in the inner layers is called “hidden layers”. The hidden layers calculate the output based on the “weights” which is the “sum of weighted synapse connections”. The hidden layers refine the input by removing redundant information and send the information to the next hidden layer for further processing.
• Output Layer: This hidden layer connects to the “output layer” where the output is shown.

Activation Function

The activation function is an internal state of a neuron. It is a function of input that the neuron receives. The activation function is used to convert the input signal on the node of ANN to an output signal.

Types of Artificial Neural Networks

• Feedforward Neural Network – Artificial Neuron
• Radial basis function Neural Network
• Kohonen Self Organizing Neural Network
• Recurrent Neural Network(RNN) – Long Short Term Memory
• Convolutional Neural Network
• Modular Neural Network

Practical Applications for Artificial Neural Networks (ANNs)

Artificial neural networks are paving the way for life-changing applications to be developed for use in all sectors of the economy. Artificial intelligence platforms that are built on ANNs are disrupting the traditional ways of doing things. From translating web pages into other languages to having a virtual assistant order groceries online to conversing with chatbots to solve problems, AI platforms are simplifying transactions and making services accessible to all at negligible costs.

Artificial neural networks have been applied in all areas of operations. 

• Email service providers use ANNs to detect and delete spam from a user’s inbox; 
• Asset managers use it to forecast the direction of a company’s stock; 
• Credit rating firms use it to improve their credit scoring methods; 
• E-commerce platforms use it to personalize recommendations to their audience; chatbots are developed with ANNs for natural language processing; 
• Deep learning algorithms use ANN to predict the likelihood of an event; 

The list of ANN incorporation goes on across multiple sectors, industries, and countries.