What Is an Artificial Neural Network?

As you develop your career in data science or engineering, you’ll eventually encounter an artificial neural network (ANN). It’s a computational model that simulates how neural networks work in the brain. An ANN uses a deep learning algorithm to make this all possible! 

Learn the answer to the question, “What is an artificial neural network?” below and discover the many benefits of this technology. 

ANNs, Explained

So, why do data engineers create ANNs? Why do data scientists use them for data modeling? Well, ANNs help computers learn new things and implement tasks that regular linear programs can’t. This computational model can make a system perform human-like decisions, which is an incredible prospect for various use cases. 

ANNs might seem like a new concept, but they date back to the 1940s, when Warren McCulloch and Walter Pitts proposed the first digital node or “artificial neuron.” Arranging these nodes in several layers so that each layer executes a different calculation is the foundation of ANNs today. Modern ANNs contain multiple artificial neurons that perform similarly to the ones in the brain. That makes this technology capable of calculating complex data sets. 

Thinking of an ANN as a neural network inside the human brain will help you understand this technology. Biological neurons use chemical signals and electrical impulses to send information to different parts of the brain and other areas of the nervous system. An ANN works similarly. Digital or artificial neurons use deep learning algorithms to send information to a computer system. This process happens when the artificial neural network gives each connection between neurons a “weight,” transforming input data within the network’s layers. 

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Benefits of ANNs

There are numerous benefits of ANNs, including: 

The Ability To Perform Visual Analysis

Because an ANN simulates human neural networks, this technology can facilitate more complex tasks than other computational models. These tasks include analyzing visual information and separating that data into categories, enabling data scientists to glean valuable insights for the companies they work for. 

Flexible Structure

An ANN provides more flexibility than machine learning algorithms and can change its structure as it becomes more intelligent, similar to biological neural networks. As a result, data engineers don’t always have to reprogram an ANN, and scientists can generate more insightful analyses using this model. 

Processing Unorganized Data

Data engineers will benefit significantly from an ANN. This computational model can process unorganized data, removing the need to perform this task manually. Unorganized data is data that either does not have a predefined data model or is not organized in a predefined way, making it difficult for engineers to handle. An ANN can solve this problem by categorizing and structuring data and improving data management outcomes.

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Drawbacks of ANNs

ANNs will continue to support data science and engineering workflows in the years to come. However, there are a few drawbacks to this technology:

  • Creating an ANN requires expensive hardware and computational power that many organizations can’t afford. 
  • An ANN is dependent on the data that engineers feed it. That can result in bias and other adverse outcomes.
  • Because an ANN can change its structure over time, engineers don’t have as much control over this model as machine learning algorithms and other technologies. 

Final Word

You will get more detailed answers to the question, “What is an artificial neural network” as you progress in your data engineering or science career. However, this glossary entry helps you understand the basics of this technology and its benefits and drawbacks. From their origin in the 1940s, ANNs have provided engineers and scientists with exciting prospects for managing and analyzing data.

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