Everything You Need to Know about Artificial Neural Networks (ANNs)

Artificial neural networks (ANNs) are capable of learning information and processing data in a similar fashion to humans. However, an artificial neural network (ANN) relies on deep learning and often carries configuration to generate specific features for certain applications. For example, pattern recognition by multiple learning processes.

In humans, learning occurs in connection with the biological systems, such as the synaptic connections between the neurons in the brain. With ANNs, it is somewhat similar.

What are Artificial Neural Networks?

ANNs are computational pieces of systems that carry specific designs to carry out tasks involved in learning and analyzing large sets of data, just like the human brain. ANNs are at the base of Artificial Intelligence (AI) and process information that is impractical on statistical or human standards.

You should know that ANNs comprise machine learning and self-learning capabilities that lead to better results with the availability of more data.

Understanding the ANNs

ANNs have neuron nodes that lie in an interconnection similar to a web format. This is also similar to the human brain that contains billions of neuron cells distributed across the body. The neuron cells in the human body are cell bodies that can process information by receiving input (sensory stimuli) and producing output (reaction).

Similarly, hundreds of thousands of artificial neurons exist in a single artificial neural network. These are the processing units constituting input units that obtain information from numerous sources. Keep in mind, the processing of the information collected in an ANN is an internal weight system-based.

Furthermore, an ANN’s processing unit processes this information to yield results and reports through the output units. Nevertheless, in ANNs, there are specific sets of rules called “backpropagation (or backward propagation),” just like human brains that guide the perfection of output reports and results.

Applications of ANNs

The list of Artificial Neural Network’s applications is extensive, but there are certain common actions and applications that you might know. Some of the applications are below.

• Asset Management to predict any movements or unusual fluctuations in a stock market.
• Deep learning and machine learning algorithms utilize ANNs to generate predictions and consequences of any likely events.
• Credit rating companies and businesses utilize self-learning ANNs to enhance and better their credit scoring techniques and processes.
• Email service makes use of self-learning ANNs to identify any spam or malicious emails and delete them right away for an email user’s convenience.
• Chatbots engage with potential clients and candidates online (virtually) by making use of ANNs.
• E-commerce websites and platforms improve their recommendation and suggestion systems by incorporating ANNs.

There are a lot more uses and applications of ANNs for various industries, sectors, and countries.

Limitations of ANNs

Despite ANNs’ adaptability and self-learning features, it also experiences certain limitations in its processes.

• Emulation is crucial because the artificial neurons are basically microprocessors rather than biological and synaptic neuron cells.
• ANNs require training initially in order to begin effective functions
• Huge neural networks involve longer processing time periods

Conclusion

The world can benefit from the long list of applications that ANNs supports. Well, they can solve complex problems associated with intricate and often irrelevant pieces of information. Not to mention, for a human to conduct such a job, it would require a lot more energy and time.