Neural Networks and Industry Use Cases

Neural networks ???

Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning.

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

Types Of Neural Networks...

Feedforward neural networks,or multi-layer perceptron's (MLPs), are what we’ve primarily been focusing on within this article. They are comprised of an input layer, a hidden layer or layers, and an output layer. While these neural networks are also commonly referred to as MLPs, it’s important to note that they are actually comprised of sigmoid neurons, not perceptron's, as most real-world problems are nonlinear.

Convolutional neural networks (CNNs) are similar to feedforward networks, but they’re usually utilized for image recognition, pattern recognition, and/or computer vision. These networks harness principles from linear algebra, particularly matrix multiplication, to identify patterns within an image.

Recurrent neural networks (RNNs) are identified by their feedback loops. These learning algorithms are primarily leveraged when using time-series data to make predictions about future outcomes, such as stock market predictions or sales forecasting.

How do neural networks work?

Think of each individual node as its own linear regression model, composed of input data, weights, a bias (or threshold), and an output. The formula would look something like this:

Examples of Neural Network Business Applications

Neural networks are widely used in different industries. Both big companies and startups use this technology. Most often, neural networks can be found in all kinds of industries: from eCommerce to vehicle building.

So, let’s look at some examples of neural network applications in different areas. Mostly, in:

• eCommerce;
• Finance;
• Healthcare;
• Security;
• Logistics.

 eCommerce

This technology is used in this industry for various purposes. But the most frequent example of artificial neural network application in eCommerce is personalizing the purchaser’s experience. For instance, Amazon, AliExpress, and other eCommerce platforms use AI to show the related and recommended products. The compilation is formed on the basis of the users’ behavior. The system analyzes the characteristics of certain items and shows similar ones. 

Finance

In this industry, there are neural network applications for fraud detection, management, and forecasting. Let’s look at some samples.

A great example of neural network finance applications is SAS Real Time Decision Manager. It helps banks to find solutions for business issues (for instance, whether to give credit to a certain person) analyzing risks and probable profits.

Healthcare

It is very difficult to create and train a neural network for usage in this industry because it requires high accuracy. For many years it seemed to be a fantasy to use this technology for examining patients and diagnosing them. But finally, it has become possible.

IBM Watson is the most powerful artificial intelligence in the world. It took 2 years to train the neural network for medical practice. Millions of pages of medical academic journals, medical records, and other documents were uploaded to the system for its learning. And now it can prompt the diagnosis and propose the best treatment pattern based on the patient’s complaints and anamnesis.

Security

Neural networks are widely used for protection from computer viruses, fraud, etc.One of the examples is ICSP Neural from Symantec. It protects from cyber attacks by determining the bad USB devices containing viruses and exploiting zero-day vulnerabilities.

Vehicle building

AI and ML are used in this industry to automate processes. For example, Tesla uses a neural network for the autopilot system in the vehicles. With the help of trained artificial intelligence, it recognizes the road markings, detects obstacles, and makes the road safer for the driver.

Wrap up

As you can see, AI and ML are the future of all the industries. These technologies help to make decisions, automate the working processes, prevent fraud, and do other important tasks. And they will continue developing...

Thank You for reading :)