Neural Network Examples, Applications, and Use Cases

A neural network is a type of artificial intelligence that allows machines to think similarly to humans by making “organic” connections through preexisting knowledge and learning from experience. One popular example of neural networks in use is the self-driving car, which needs to make decisions about and react to a wide number of random variables at any given moment.

Microsoft CEO Satya Nadella named 2023 the “Year of AI” due to the breakthroughs and gains the field of artificial intelligence made over the year. While it’s true that AI growth in 2023 was expansive, the first neural network was created in 1958 by research psychologist Frank Rosenblatt, nearly 70 years ago [1]. Called the perceptron, Rosenblatt’s rudimentary invention created a foundation for the field that ultimately led to neural networks as we understand them today.

Use this article to discover neural network examples to help you conceptualize how the technology works and the many neural network applications that may be possible across industries. 

What is a neural network?

A neural network is a machine learning system that attempts to mimic the way human intelligence works to power AI. It's structured using nodes arranged in layers that filter data and transfer information through the system to make connections.

The network user creates an input, and the neural network delivers an output. However, under the surface, the input filters through a system of hidden layers, where the nodes carry varying weights to add complexity and nuance to the machine’s understanding of that input. The more layers within the neural network, the more points of consideration the neural network will use to create the output.

Neural networks are useful tools for open-ended or general problems where the associations between the variables aren’t obvious or easy to label. When you offer nonlinear or complicated data to the neural network, the technology can discover and model how the data relates.

Neural network examples

A neural network simulates the way humans think. It’s no surprise that neural networks are versatile since our brains are also so versatile. Below, you will find examples of different technologies that neural networks contribute to, applications in specific industries, and use cases for companies using neural networks to solve problems.

Neural network examples: Technology

A neural network acts as a framework, supporting how artificial intelligence will operate and what it will do with the data presented to it. As a framework, it powers specific technologies like computer vision, speech recognition, natural language processing, and recommendation engines, giving us specific use cases for neural network technology. Let’s take a closer look at each of these AI fields.

Computer vision

Computer vision allows artificial intelligence to “look” at an image or video and process the information to understand and make decisions. Neural networks make computer vision faster and more accurate than was previously possible because a neural network can learn from data in real time without needing as much prior training. Much like human vision, artificial intelligence can use computer vision to observe and learn, classifying visual data for a broad range of applications. 

Speech recognition

Speech recognition allows AI to “hear” and understand natural language requests and conversations. Scientists have been working on speech recognition for computers since at least 1962. But today, advancements in neural networks and deep learning make it possible for artificial intelligence to have an unscripted conversation with a human, responding in ways that feel natural to a human ear. You can also use neural networks to enhance human speech, for example, during recorded teleconferencing or for hearing aids.

Natural language processing

Natural language processing (NLP) is similar to speech recognition. In addition to understanding and interpreting spoken requests, NLP focuses on understanding text. This technology enables AI chatbots like ChatGPT to have a written conversation with you. Neural networks allow computer scientists to train NLP systems much faster because they do not have to hand code and train the algorithm.

Recommendation engines

A recommendation engine is an AI tool that suggests other products or media you might like based on what you’ve browsed, purchased, read, or watched. With neural networks, a recommendation engine can gain a deeper understanding of consumer behavior and offer further targeted results that are likely to interest consumers. Recommendation tools can help encourage customers to stay more engaged on a website and make it easier for them to find items they like. 

Neural network examples: Applications

All the technologies mentioned above benefit from neural network artificial intelligence. In practice, these areas of artificial intelligence offer many uses. A few specific neural network examples include:

• Medical imaging: Healthcare professionals can use neural networks to read medical images, such as X-rays or MRIs. Artificial intelligence can analyze a medical image incredibly fast compared to a human professional and can continuously analyze images night and day, unlike a person constrained by human needs like hunger and fatigue.

• Self-driving cars: Neural networks power self-driving cars. While on the road, these cars must be aware of many different variables happening simultaneously and randomly. In this environment, artificial intelligence also needs to make decisions based on the information it receives. A neural network enables the complex thinking a self-driving vehicle requires.

• Public safety and security: Neural networks also offer various solutions for public safety and security. For example, artificial intelligence can be used for fraud detection, traffic accident detection, or predicting suspicious or criminal behavior.

• Agriculture: In agriculture, farmers can use artificial intelligence for tasks like irrigation, pest control, predicting weather patterns, and choosing seeds optimized for their growing area. For these tasks, the artificial intelligence will need sensors to help it gain more information about the growing conditions—for example, a sensor to detect moisture levels in soil.

• Online content moderation: Neural networks can detect online content that goes against community standards, acting as a quick and effective content moderator that never stops working. In fact, Meta reported in 2021 that it uses artificial intelligence to flag 97 percent of the content it removes from Facebook for community standards violations [2].

• Voice-activated virtual assistants: Using speech recognition technology, the neural network at the center of your voice-activated virtual assistant can understand what you say to it and respond accordingly. With the advanced ability of neural networks, voice-activated virtual assistants can also understand the tone and context of what you say.

• AI subtitles: Speech recognition and natural language processing together make it possible for artificial intelligence to automatically subtitle a video by listening to and understanding speech, and then translating it into a text caption.

Neural network use cases

We’ve discussed technologies and applications for neural networks, but what are some examples of companies using neural networks for solutions specific to their industries? Let’s take a look at some solutions from Google and IBM:

• You can use Google Translate to automatically translate the text contained in an image. For example, you could take a picture of a street sign or handwritten note, and Google Translate will scan it and provide a translation.

• In 2018, IBM Watson used neural networks to create customized highlight reels of the Masters golf tournament. Users could curate the highlights they saw based on their preferences, taking advantage of a spoiler-free mode that would avoid ruining the cliffhanger moments.

• In a partnership between IBM Watson, Quest Diagnostics, and Memorial Sloan Kettering Cancer Center, artificial intelligence bolstered by neural networks began reviewing lab results from cancer patients to provide genetic testing. Comparing the results against a vast library of cancer-related research, the AI then suggests the best course of individualized treatment. An AI agent can complete this work in a fraction of the time it takes a human health care professional.

Learn more with Coursera

If you’re ready to discover more about the concept of neural networks, consider the course Neural Networks and Deep Learning offered by DeepLearning.AI on Coursera. With this course, you can learn about artificial neural networks, deep learning, and neural network architecture, among other topics.