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What are neural networks and how are they applied in business?

Posted January 31, 2022 in News

Written by Terry May, Xpanxion Technical Writer

AI and machine learning nodes

Neural networks are advancing exponentially in real-world business applications as organizations digitally evolve.

Businesses across a broad range of industries use neural networks in various ways to solve complex problems. These include recognizing patterns and sequences that are too complex for humans to identify, classifying and clustering volumes of data at high velocity, and predicting outcomes.

But first, it’s almost impossible to describe neural networks without describing machine learning (ML) and deep learning (DL). This is because neural networks are a subset of ML, and they’re at the heart of DL algorithms. Neural networks and deep learning are often used interchangeably, but to be clear, the “deep” in deep learning is simply referring to the depth of layers in a neural network. A neural network is also called an artificial neural network (ANN).

Machine learning defined: ML allows machines (computers) to automatically learn and improve from experience without being explicitly programmed to do so. They contain different algorithms (a set of mathematical models) to solve different problems; a neural network is just one of those sets of algorithms.

How neural networks work

A neural network is a ML algorithm that trains computers to think and learn like humans by loosely emulating the way neurons function in the human brain.

Neural networks consist of multiple layers called nodes:

The function of the hidden layer or layers is to work on (process and analyze) the data that was developed by the previous layer, then link to the output layer to reveal the final decision or prediction. By processing data through hidden layers, the network continues to self-learn more about the data.

Each node has a predetermined synaptic weight and a threshold value. The heavier the weight or the higher the number, the more influence it has on another node. If the output of any node exceeds the defined threshold value, that node is triggered to send data to the next layer. If not, no data is sent to the next layer.

Each node also contains two sets of rules:

  1.  The rules that the network was initially programmed with
  2.  The rules the system has learned on its own. This allows the network to learn and respond to both structured and unstructured data.

The way neural networks work is how they learn and improve generation after generation without human input. How awesome is that?! Well, the truth is neural networks are not perfect, at least not at first.

The first deployment will not present accurate results because it hasn't yet been trained. It will take some time for the neural network to learn before it can be released in the real world. And for neural networks to work at all, they require massive volumes of training data which is hard to come by. But that hasn’t stopped digitally driven companies from increasingly finding value from a vast array of neural network applications—and setting the market for explosive growth.

The business case for neural networks

The market for real-world neural network applications is booming; projected to reach an impressive $22.55 billion by 2021 at a CAGR of 33%.

Well-known neural networks can be found in the popular speech recognition systems in Amazon Alexa and Google Assistant. There are plenty of other examples in our daily lives that amplify the growth of neural networks in business—from self-driving cars, to mobile robots, to AR/VR games, to robotic process automation (RPA), and many more.

IBM Watson Health is a profound example of neural networks in business because it has earned the title as the most powerful artificial neural network in the world. It took two years to train the network using millions of pages of medical records, academic journals, and other documents. Today it can perform patient diagnostics, identify symptoms, and propose treatments; read X-rays and MRIs at lightspeed to zero in on potential ailments; speed up DNA analysis in cancer patients; discover new drugs, and much more. Fun fact: it’s also used in veterinary medicine.

Machine learning and neural networks are the future of all industries. Here are examples of how they are applied in just some industries:

  • Finance for fraud detection, management and forecasting
  • E-commerce and retail for fulfillment and personalizing the consumer experience
  • Security for protection from computer viruses, fraud, etc.
  • Manufacturing for defect detection on the assembly line
  • Logistics for routing and dispatching
  • Buidling and construction for controlling the heating, ventilation and air conditioning (HVAC) system
  • Marketing for predicting the outcomes of campaigns

And this is just the beginning of neural networks radically changing and improving the way our world works. The best part is that they will continue to develop … making more critical decisions and predictions, automating more work processes, and other valuable tasks.

So, are you wondering how neural networks and ML can boost your business? Talk to our experts to learn how to get started. Explore our success stories, solutions and platforms to see how we prepare companies for the future of all industries.


About Xpanxion - Solving business problems with technology. We are software product engineering experts with over 20+ years of experience delivering the technologies, software architectures, processes and people critical to delivering success. As a trusted partner, we focus on business solutions and alliances that provide end-to-end value to solving our customer’s problems. We focus on providing best-in-class solutions by developing custom solutions with modern technologies or by delivering industry recognized off the shelf solutions.

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