This is the age of supercomputers, Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning where computing power has gone much beyond our imagination. With such scientific and technological advancements, it has become possible to process huge volumes of data within a fraction of a second for getting valuable insights.
A common term, which is extensively used when we talk about AI or ML is Artificial Neural Network (ANN). Artificial Neural Network is a model which imitates the way in which various nerve cells function in the human brain. There are many aspects and interesting layers in this network. Read on to learn more about Artificial Neural Networks, how they work, their advantages and other related things.
What Are Artificial Neural Networks?
Artificial Neural Networks refer to a subfield of Artificial Intelligence, modelled after the human brain. These are algorithms which are based on brain functions for forecasting issues and modelling complicated patterns. The Deep Learning method emanates from the concept of biological neural networks.
The main aim of developing Artificial Neural Networks was attempting to replicate the functioning of the human brain. If you study closely, you will find that the functioning of ANN is similar to that of biological neural networks. ANN algorithms work on structured and numeric data.
The learning algorithms that ANNs use can make adjustments independently. Precisely, they can learn as they receive any kind of new input. This is the reason they are highly effective for non-linear statistical data modelling.
The Architecture of Artificial Neural Networks
To understand Artificial Neural Networks, you must have knowledge about the architecture of ANNs.
Layers in the Artificial Neural Network
There are three or more layers in an Artificial Neural Network, which are interconnected to one another, namely the input layer, hidden layer and output layer. Because of multiple layers, ANNs are often called Multi-layer Perceptron (MLP).
The first layer has input neurons. These neurons send data to the other deeper layers in the network. After the data is processed through these layers, the final output data is sent to the last output layer.
In the ANN, all inner layers are hidden. These layers are made of units which adaptively alter the data received from one layer to another through a long series of transformations. The hidden layer is also referred to as the ‘distillation’ layer as the most relevant and useful pattern is extracted from the inputs and sent for further analysis to the next level. Redundant information gets discarded in the process.
Each layer in the ANN functions as both an input and output layer for understanding complex subjects well. Collectively, all the inner layers are called neural layers.
Using backpropagation
Using backpropagation is an essential part of Artificial Neural Networks. Backpropagation is a process in which the ANN adjusts the output results by considering the errors in the account.
During the supervised training phase, every time output is labelled as an error, it is sent backwards through the process of backpropagation. Every single weight is updated to understand how it contributed to the error.
The error recalibrates the weight of the ANNs unit connections for understanding the difference between the actual outcome and the desired outcome. The ANN will gradually learn to minimise the chances of unwanted results and reduce the number of errors as well.
Practical Applications of Artificial Neural Networks
Artificial Neural Networks have unique properties and therefore they are used in various applications. Some of the practical applications of Artificial Neural Networks are as follows:
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Forecasting
Artificial Neural Networks help in making forecasts which impact business decisions greatly. Regular business decisions include capacity utilisation, the financial allocation between goods, sales, etc.
Apart from these ANNs also help in making forecasts on the stock market, monetary and economic policy, finance and other subjects. Forecasting is quite a complex thing as there are many underlying variables concerned, some of which are known and some are unknown.
There are things that could be improved in traditional forecasting models. On the other hand, ANNs can extract previously known correlations and unknown characteristics, thus providing accurate forecasts with minimal errors.
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Image Processing and Character Recognition
Artificial Neural Networks have a significant role in character and picture recognition. This is because these networks can take in innumerable inputs, process them and derive complicated and hidden, non-linear correlations.
For instance, handwriting recognition is an integral part of character recognition and is used extensively in various applications like fraud detection and national-level security assessments.
Apart from character recognition, image recognition is a fast-evolving discipline. Image recognition is now used in various applications right from cancer detection in medical science to facial identification in social media, from defence purposes to satellite image processing, agricultural purposes and so on.
Deep Learning includes deep neural networks, which have opened up transformative and new advances in natural language processing, speech recognition and computer vision. One of the best examples in this regard is self-driving vehicles.
Advantages of Artificial Neural Networks
Some of the most prominent advantages of Artificial Neural Networks are as follows:
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Data storage on the entire network
Data used in traditional programming is not stored on a database; but rather on the whole network. When a few pieces of data disappear in one place, it does not prevent the network from functioning.
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Parallel processing capability
Artificial Neural Networks have the capability of performing more than one task simultaneously. This is possible because ANNs have a numerical value.
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ANNs can function with incomplete knowledge
Post-ANN training, the output will be produced from the given information even with inadequate data. The loss of performance depends upon the significance of missing information.
Summing it up
Artificial Neural Networks have myriad applications and are being extensively used in natural language processing, spam email detection, predictive analysis in business intelligence, chatbots and so on.
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