The whole idea behind artificial intelligence is to make a machine act like a human being. While many sub-divisions of AI originated with their own set of algorithms to mimic humans, artificial neural networks (ANNs) are AI at its purest sense; they mimic the working of the human brain, the core and complex foundation which influences and affects the thinking and reasoning of human beings.

What Is an Artificial Neural Network?

ANN is a machine learning algorithm. It is founded on the scientific knowledge about organic neural networks (working of the human brain). ANN works quite similar to how human beings analyze and review information. It is composed of several processing units which are linked together and perform parallel processing for the computation of data.

As machine learning is primarily focused on “learning,” ANNs continuously learn and adapt. The processing units in ANNs are commonly referred to as neurons or nodes. Bear in mind that neuron in biology refers to the most basic units in the human nervous system. Each node is linked via arcs which have their own weight. The artificial neural network is made up of three layers.

Input

The input layer is responsible for accepting explanatory attribute values which are collected from observations. Generally, input nodes are explanatory variables. Patterns are submitted to the network by the input layer. Subsequently, those patterns are then analyzed by the hidden layers. The input layer nodes are not involved in modifying any data. They accept individual values as inputs and then perform duplication of the value so it can be passed on to multiple outputs.

Hidden

The hidden layers modify and transform values collected from the input layer. By utilizing a technique of weight links or connections, the hidden layer initiates computation on the data. The number of hidden layers depends upon the artificial neural network; there may be one or more than one hidden layers. Nodes in this layer multiply the collected values by the weights. Weights are a predetermined set of numbers which convert the input values with the help of summation to generate an output in the form of a number.

Output

Afterward, the hidden layers are connected to an output layer which may also receive a connection directly from an input layer. It generates a result, which is associated with the response variable’s prediction. Generally, when the machine learning process is geared towards classification and its disciplines, there is a single output node. The collected data in the layer is integrated and modified for the generation of new values.

The structure of a neural network is also called topology or architecture. All the above layers of the ANN form the structure. The planned design of the structure bears utmost importance to the final findings of the ANN. At its most basic, a structure is divided into two layers which are comprised of one unit each.

The output unit also possesses two functions: combination and transfer. When there are multiple output units, then logistic or linear regression can be at work and the nature of the function ultimately decides it. ANN’s weights are actually coefficients (regression).

So what do the hidden layers do? Well, the hidden layers are incorporated into ANNs to enhance the prediction strength. However, it is recommended to add them smartly because excessive use of these layers may mean that the neural network stores all the learning data and may not able to generalize, causing an over-fitting problem. Over-fitting arises when the neural network is not able to discover patterns and is heavily reliant on its learning set to function.

 

Applications

Due to their accurate predictions, ANNs have broad adoption across multiple industries.

Marketing

Modern marketing focuses on segmenting customers within well-defined and distinct groups. Each of these groups exhibits certain characters that are reflecting of its customer habits. In order to generate such segmentation, neural networks present themselves as an efficient solution for predicting strength to identify patterns in a customer’s purchasing habits.

For instance, it can analyze how much time customers take between each purchase, how much do they spend, and what do they mostly purchase. ANN’s input layer takes all the attributes like location, demographics, and other personal or financial information about a customer to generate meaningful output.

Supervised neural networks are usually trained to comprehend the link between clusters of data. On the other hand, unsupervised neural networks are used for segmentation of customers.

Forecasting

Forecasting is a part and parcel of a varied list of domains including governments, sales, finance, and other industries, especially their use in the monetary and economic aspects. Often, forecasting faces a tumbling roadblock because of its complexity. For instance, the prediction of stocks is considered difficult because the stock market addresses multiple seen and unseen factors where traditional forecasting becomes ineffective.

This conventional forecasting is founded merely on statistics. ANNs use the same statistical methods and techniques and enhances forecasting where its layers are sophisticated enough to tackle the complexity of the stock market. Moreover, in contrast to the conventional methods, ANN is non-restrictive for input values and residual distributions.

Image Processing

Since the layers in artificial neural networks are able to accept several input values and compute them flexibly to determine complex and non-linear hidden relationships, they are well-equipped to serve in image processing and character recognition. In criminal proceedings like bank frauds, fraud detection requires accurate results for character recognition because humans cannot go over thousands of samples to pinpoint a match. Here, ANNs are useful as they are able to recognize the smallest of irregularities. Similarly, ANN is used in facial recognition with positive results where they are able to improve governance and security.

Final Thoughts

The emergence of artificial neural networks has opened a whole new world of possibilities for machine learning. With their adoption in real-world industries, the algorithm has become one of the most trending and research topics in a short period of time.