The Workings of Artificial Neural Networks

For this subject to become more lucid, let's first look at what an artificial neural network is. The term Neural has its source from the medical nervous system with a basic unit known as the neuron. However, if a similitude is imbibed into an artificial set of things capable of stimulating the same behavior, that’s the origin of an Artificial Neural Network.

Artificial Neural Networks (ANN) are really popular in today's world and are useful in chat-bots typically used in the text classification. Neural networks in the software industry date back decades and continue to develop in today's fast-paced world.

How Artificial Neural Networks Work

ANN refers to biologically inspired simulations that are performed on the computer to carry out a number of tasks, including classification, pattern recognition, clustering and more. Generally speaking, ANN is a biologically inspired group of neurons (artificial) developed to take on certain tasks.

Artificial Neural Networks function similar to human biology and the neurons are like nodes. The portion handling data processing of a neuron receives many signals, both from other neurons and external sources.

Signals sometimes are modified at the receiving junction, and the weighted inputs are summed at the processing element. If it surpasses the threshold, it goes to the other neurons as input (or as output to the external world) and this process is repeated with every signaling.

Activation functions such as the Sigmoidal, Binary and Tan hyperbolic sigmoidal exist.

The Architecture of Artificial Neural Networks

In understanding how ANNs function, we first need to figure out the content of a typical neural network. In describing such networks, they contain large quantities of artificial neurons known as units arranged in a series of layers. Let's consider the various architectures of ANNs:

Input layer

This consists of artificial neurons (called units) that receive input from the external world. Here, the actual learning and recognition on the network occurs.

Output layer

This comprises units which respond to the information received by the system and also whether it learned any task or not.

Hidden layer

This is between input and output layers and only functions in the transformation of the input into meaningful content which can be used by the outer layer.

A large percentage of the ANNs are all interlinked, implying that all hidden layers are connected to both input and output layers. This allows for a complete learning process and also for it to occur maximally when the weights within the artificial neural network get updated after every repetition.

Difference Between Human Brain and Neural Network

The major contrast is humans are forgetful, neural networks are not. With complete training, a neural network will never forget. Whatever it learns is hard coded and becomes permanent.

Another difference lies in accuracy. Once the automation of a specific application or process is achieved through a neural network, the results are repeatable and accurate. Even if the process is replicated thousands of times, the results will be the same and will be as accurate as calculated initially. Humans are prone to errors and every repetition leaves room for more errors.

One other important difference is speed. Neural networks are either hardware or software. The neutral network processes data and information at speeds greater than the human brain capability.

Artificial Neural Network Applications

There are numerous areas in which artificial neural networks can be applied. Some include:


● Facial recognition
● Stock market prediction
● Social Media
● Aerospace
● Defense
● Weather forecasting
● Healthcare
● Signature verification and handwriting analysis

Conclusion

Artificial neural networking promises a future for computer science like none ever seen before. Once neural networks are trained fully and accurately, they can take over human jobs in specific or needed areas.

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